Semanlink - Amérique

Parents:

Géographie

Amérique

Related Tags:

Descendants

Amérindien
- Civilisations précolombiennes
  - Archéologie amazonienne
  - Civilisation de Caral
  - First Americans
    - Pedra Furada
  - Nasca
- Indiens du Brésil
  - Archéologie amazonienne
    Brésil : mort de l’« Indien du trou », dernier survivant du peuple indigène Tanaru
    Tags:
    
    Brésil
    Indiens du Brésil
    2022-08-30 About
- Yagán
Amérique latine
- Amérique centrale
  - Guatemala
- Amérique du sud
  - Argentine
    - Film argentin
    - Lionel Messi
    - Terre de Feu
      - Yagán
  - Brésil
    - Amazonie
      - Archéologie amazonienne
      - Ofir
        
        Bacuri
        
        Tags:
        
        Amazonie
        Fruit
        Ofir
        
        2023-09-14 About
        
        Correio Bragantino News - CB NEWS :: Bragança - Pará - Brasil
        
        Tags:
        
        Ofir
        
        2014-01-18 About
        
        chef ofir oliveira | Colheres na Estrada
        
        Tags:
        
        Ofir
        
        2014-01-18 About
        
        Instituto Amazônia Brasil: Ofir
        
        Tags:
        
        Amazonie
        Ofir
        
        2012-01-07 About
        
        Brazil - Go Fish - Which Fish? | Slow Fish - Local Sustainable Fish
        
        Tags:
        
        Ofir
        Slow food
        
        2011-02-01 About
        
        Cozinheiro brasileiro Ofir Oliveira veio ao Douro para encantar
        
        Tags:
        
        Gastronomie
        Ofir
        
        2008-11-28 About
        
        Chefes Rui Paula e Ofir Oliveira unem sabores selvagens da Amazónia aos tradicionais do Douro
        
        Tags:
        
        Amazonie
        Gastronomie
        Ofir
        Portugal
        
        2008-11-28 About
        
        Milton Santos - Wikipédia, a enciclopédia livre
        
        Tags:
        
        Brésil
        Géographie
        Ofir
        
        2008-11-10 About
        
        Sabor Selvagem
        
        Tags:
        
        Belém
        Ofir
        
        Não se trata exatamente de um restaurante, e, sim, de uma enorme sala de jantar no quintal de Ofir Cavalcante, um dos chefs mais criativos do Pará. Como não existe cardápio, o cliente paga uma quantia fixa com direito a entrada, prato principal e sobremesa
        
        2007-09-11 About
        
        Revista Nosso Pará - On line
        
        Tags:
        
        Gastronomie
        Ofir
        Pará
        Restaurant
        
        2007-09-11 About
      - Pará
        Belém
        
        Sabor Selvagem
        
        Tags:
        
        Belém
        Ofir
        
        Não se trata exatamente de um restaurante, e, sim, de uma enorme sala de jantar no quintal de Ofir Cavalcante, um dos chefs mais criativos do Pará. Como não existe cardápio, o cliente paga uma quantia fixa com direito a entrada, prato principal e sobremesa
        
        2007-09-11 About
        
        Revista Nosso Pará - On line
        
        Tags:
        
        Gastronomie
        Ofir
        Pará
        Restaurant
        
        2007-09-11 About
    - Belo Horizonte
    - Bolsonaro
    - Caetano Veloso
    - Cazuza
    - Chico Buarque
      - Fado tropical
        
        Fado tropical de Chico Buarque et Portugal de Georges Moustaki. De la dictature de Salazar à la Révolution des œillets au Portugal
        
        Tags:
        
        Chico Buarque
        Dictature
        Fado tropical
        Portugal
        
        2020-05-29 About
      - Ópera do Malandro
        
        Elba Ramalho e Claudia Ohana em "O Meu Amor"
        
        Tags:
        
        Ópera do Malandro
        YouTube video
        
        Memorável cena do filme "Ópera do Malandro", em que Elba Ramalho e Cláudia Ohana se enfrentam.
        
        2008-11-14 About
    - Filme brasileiro
      - Ópera do Malandro
        
        Au Brésil, la redécouverte miraculeuse de l’un des plus anciens films tournés en Amazonie
        
        Tags:
        
        Amazonie
        Bonne nouvelle
        Filme brasileiro
        
        2023-11-27 About
        
        Ilha das Flores (curta-metragem)
        
        Tags:
        
        Filme brasileiro
        Inégalités
        Robert
        Vive le capitalisme !
        
        2020-11-27 About
        
        Aquarius (filme)
        
        Tags:
        
        Filme brasileiro
        Sônia Braga
        
        2019-02-27 About
        
        Cidade de Deus (filme) – Wikipédia, a enciclopédia livre
        
        Tags:
        
        Filme brasileiro
        
        2013-08-28 About
    - Histoire du Brésil
    - Indiens du Brésil
    - Joaquim Maria Machado de Assis
    - Lava-jato
    - Lula
    - Musique brésilienne
      - Caetano Veloso
      - Gilberto Gil
        
        Gilberto Gil and Caetano Veloso in London | Music | The Guardian
        
        Tags:
        
        Caetano Veloso
        Gilberto Gil
        Londres
        
        2018-10-25 About
      - Marisa Monte
        
        Chanson : Marisa Monte, beauté et volupté
        
        Tags:
        
        Marisa Monte
        
        > La chanteuse carioca publie le somptueux « Portas », premier album sous son seul nom depuis dix ans.
        
        2021-08-13 About
      - Samba
        
        Bloco do Sargento Pimenta
        
        Tags:
        
        Beatles
        Musique
        Rio de Janeiro
        Samba
        WWW 2013
        
        2013-05-21 About
        
        Rio Scenarium
        
        Tags:
        
        LDOW2013
        Rio de Janeiro
        Samba
        
        2013-05-15 About
        
        "O carnaval É a maior caricatura Na folia O povo esquece a amargura" (Salgueiro - Samba-enredo 1983)
        
        Tags:
        
        Carnaval
        Rio de Janeiro
        Samba
        YouTube video
        
        2009-06-14 About
    - Pará
    - Pedra Furada
    - Renato Matos
    - Rio de Janeiro
      - WWW 2013
        LDOW2013
        fps @ LDOW 2013
        
        Describing Customizable Products on the Web of Data (LDOW 2013)
        
        Tags:
        
        fps @ LDOW 2013
        fpservant@slideshare
        
        2013-05-28 About
        
        Describing Customizable Products on the Web of Data, LDOW 2013
        
        Tags:
        
        Configuration as Linked Data
        Configuration ontology
        fps @ LDOW 2013
        
        2013-04-25 About
        
        Rio Scenarium
        
        Tags:
        
        LDOW2013
        Rio de Janeiro
        Samba
        
        2013-05-15 About
        
        Building a Web of Needs, Florian Kleedorfer
        
        Tags:
        
        LDOW2013
        Web of Needs
        
        "Tell what you need, and give it a URI"
        
        2013-05-14 About
        
        Linked Data on the Web (LDOW2013) - Workshop at WWW2013, Rio de Janeiro, Brazil
        
        Tags:
        
        LDOW2013
        
        2013-03-02 About
        
        Using SKOS vocabularies for improving Web Search
        
        Tags:
        
        Bernhard Haslhofer
        SKOS
        Web search
        WWW 2013
        
        2013-05-30 About
        
        Bloco do Sargento Pimenta
        
        Tags:
        
        Beatles
        Musique
        Rio de Janeiro
        Samba
        WWW 2013
        
        2013-05-21 About
        
        Rethinking the Web as a Personal Archive
        
        Tags:
        
        http://www.semanlink.net/tag/semanlink2_related
        Microsoft Research
        Personal archives
        WWW 2013
        
        2013-05-21 About
        
        Tutorial: Linked Data Query Processing
        
        Tags:
        
        Linked Data
        Olaf Hartig
        Tutorial
        WWW 2013
        
        2013-05-18 About
        
        Live topic generation from event streams
        
        Tags:
        
        Raphaël Troncy
        WWW 2013
        
        2013-05-17 About
        
        “We need people to translate the whole Web”: Luis Von Ahn | WWW 2013 – Rio de Janeiro, Brazil
        
        Tags:
        
        Luis von Ahn
        WWW 2013
        
        2013-05-15 About
        
        An introduction to semantic web and linked data
        
        Tags:
        
        Fabien Gandon
        Semantic Web : introduction
        WWW 2013
        
        2013-05-14 About
    - Sônia Braga
  - Chili
    - Allende
    - Pablo Neruda
    - Pinochet
    - Terre de Feu
      Les dernières heures d'Allende
      
      Tags:
      
      Allende
      Chili
      Coup d'état
      Humour noir
      Nixon
      Pinochet
      
      > "Pourquoi n'y aura-t-il jamais de coup d'Etat aux Etats-Unis ? Parce qu'il n'y a pas d'ambassade américaine dans ce pays."
      
      2023-09-10 About
      Neruda (film)
      
      Tags:
      
      Chili
      Movie
      Pablo Neruda
      
      > Je suis une dame, Pablo. Je n’ai plus de sous-vêtements propres. Je ne peux pas me lever. Présentation par [ARTE](https://www.arte.tv/fr/videos/086872-000-A/neruda/) : La cavale du poète chilien Pablo Neruda, poursuivi à la fin des années 1940 par le gouvernement de Videla... Signée Pablo Larraín ("Jackie", "No"), une oeuvre onirique et audacieuse, avec Luis Gnecco et Gael García Bernal. Chili, 1948. Le gouvernement de Gabriel Gonzáles Videla lance une chasse aux communistes. Au Congrès national, le sénateur de gauche Pablo Neruda accuse le président de trahir ceux qui l’ont aidé à se hisser au pouvoir deux ans auparavant. Videla demande sa destitution et confie au redoutable inspecteur Oscar Peluchonneau le soin de procéder à son arrestation. Commence alors un jeu du chat et de la souris entre le policier et le poète... Mythe revisité Si Pablo Larraín retrace la traque subie, à la fin des années 1940, par l’intellectuel et sa seconde femme, Delia del Carril, il s’affranchit du biopic classique en entremêlant savamment réel et imaginaire. Narrée par l’obstiné Oscar Peluchonneau, brillamment campé par Gael García Bernal, cette course-poursuite haletante se mue en fable onirique : le vaniteux policier ne serait qu’un fantasme né de l’imagination de Neruda, heureux de forger sa propre légende. Des bordels de Santiago jusqu’à l’échappée finale au cœur de la cordillère des Andes, le film s’amuse à réinventer le mythe du héros adulé de toute une nation. Bluffant de mimétisme, Luis Gnecco compose un Neruda épris de justice et de liberté mais aussi provocateur et égoïste. Une œuvre iconoclaste entre polar, conte et western enneigé.
      
      2020-05-07 About
  - Equateur
  - Uruguay
- Argentine
- Chili
Antilles
- Guadeloupe
- Zouk
  - Kassav'
Brésil
Canada
Civilisations précolombiennes
Mexique
- Zapata
Pérou
- Civilisation de Caral
- Mario Vargas Llosa
- Nasca
  Au Pérou, le plus ancien complexe archéologique des Amériques menacé par des occupations sauvages
  Tags:
  Faced with a climatic crisis, the inhabitants abandoned the city around 1900 BC. When they left, they covered the pyramids with stones, hoping perhaps to find them again on their return.
  
  2022-06-16 About
  Caral
  Tags:
  - Civilisations précolombiennes
  - Pérou
  2010-06-29 About
  BBC NEWS | Peru's 'copper mountain' in Chinese hands
  Tags:
  2008-06-25 About
USA
- 11 septembre 2001
  - Guantanamo
- Alexandria Ocasio-Cortez
- Amérique profonde
- Armée américaine
  - DARPA
    - DARPA Grand Challenge
- Bush
  - Guantanamo
    Patti Smith Indicts George W. Bush - Dream of life
    Tags:
    
    Bush
    Contestation
    Patti Smith
    2009-04-19 About
- Californie
  - Silicon Valley
- CIA
- Detroit
- Diplomatie américaine
- Elections américaines 2020
- Europe vs USA
- FBI
  - FBI v. Apple
- General Motors
- Impérialisme américain
- Justice américaine
- NASA
  - Cassini
    - Cassini-Huygens
  - Hubble
  - JPL
  - Messenger
  - New Horizons
  - OSIRIS-REx
  - Voyager
- NSA
  - NSA spying scandal
    - Scandale des écoutes en Allemagne
  - PRISM
    - NSA spying scandal
      The PRISM Details Matter — PRISM Truth — Medium
      
      Tags:
      
      PRISM
      
      2013-06-13 About
      "Pourquoi stocker toutes nos vies sur des serveurs aux Etats-Unis ?"
      
      Tags:
      
      PRISM
      
      2013-06-13 About
      PRISM – Comment passer entre les mailles de la surveillance d’Internet ? | Big Browser
      
      Tags:
      
      PRISM
      
      2013-06-13 About
      Opt out of PRISM, the NSA’s global data surveillance program - PRISM BREAK
      
      Tags:
      
      PRISM
      
      2013-06-13 About
- Ohio
- Oregon
  - Multnomah Falls
  - Portland (OR)
    - Ciao Vito
- Peace Corps
  - Peter Chilson
- Pentagon
- Philipp Meyer
- Président des USA
  - Bush
  - Nixon
  - Obama
    - Obamacare
  - Reagan
  - Roosevelt
  - Trump
    - Elections américaines 2020
      Regardless of the US presidential election outcome, Trumpism lives on | The Guardian
      
      Tags:
      
      Elections américaines 2020
      The Guardian
      Trump
      USA
      
      > His victory in 2016, it turns out, was no fluke attributable to Vladimir Putin or James Comey. In 2020 his sexism, racism and lie-telling have been legitimised and emboldened. > When some Americans protested “This is not who we are”, Trump voters replied: “This is exactly who we are – and we’re not going anywhere.”
      
      2020-11-05 About
      Lil Miquela: The Digital Avatar Instagram Influencer
      
      Tags:
      
      Avatar
      Nous vivons une époque moderne
      Troll
      Trump
      
      1 million followers on Instagram and recently hacked by a Trump troll. But it's not human. La guerre des robots a commencé
      
      2018-11-05 About
      Comment Trump a manipulé l'Amérique | ARTE
      
      Tags:
      
      Cambridge Analytica
      Documentaire TV
      Trump
      
      2018-10-13 About
      How Trump Consultants Exploited the Facebook Data of Millions - The New York Times
      
      Tags:
      
      Cambridge Analytica
      Trump
      
      2018-03-18 About
      Why are palaeontologists suing Trump? | Elsa Panciroli | Science | The Guardian
      
      Tags:
      
      Paléontologie
      Trump
      
      2017-12-16 About
      Reagan puissance dix | Le blog de Thomas Piketty
      
      Tags:
      
      Reagan
      Thomas Piketty
      Trump
      
      2017-06-15 About
      Revealed: how US billionaire helped to back Brexit | Politics | The Guardian
      
      Tags:
      
      Brexit
      Social manipulation
      Social Networks
      Trump
      
      2017-02-26 About
      Robert Mercer: the big data billionaire waging war on mainstream media | Politics | The Guardian
      
      Tags:
      
      Big Data
      Mainstream media
      Trump
      
      2017-02-26 About
      The Immigration Ban is a Headfake, and We’re Falling For It – Medium
      
      Tags:
      
      Trump
      
      2017-02-01 About
      Katy Tur Talks Covering Donald Trump's Candidacy for NBC
      
      Tags:
      
      Trump
      
      My Crazy Year with Trump
      
      2017-01-21 About
      History tells us what may happen next with Brexit & Trump – Medium
      
      Tags:
      
      Brexit
      Histoire
      Trump
      
      2016-11-14 About
      A new theory for why Trump voters are so angry — that actually makes sense - The Washington Post
      
      Tags:
      
      Trump
      
      2016-11-11 About
      5 Reasons Why Trump Will Win | MICHAEL MOORE
      
      Tags:
      
      Michael Moore
      Trump
      
      2016-07-24 About
- Relations Europe-USA
  - Relations franco-américaines
- Relations franco-américaines
- San Francisco
- Tortures américaines
- Universités américaines
  - Berkeley
  - Cornell
  - Harvard
    - Harvard Business Review
    - NLP@Harvard
  - MIT
    - NLP@MIT
    - SIMILE
      - Piggy Bank
        
        Piggy Bank - Home page
        
        Tags:
        
        Piggy Bank
        
        2007-01-02 About
        
        Danny Ayers, Raw Blog : » RDF.net : Challenged!
        
        Tags:
        
        Danny Ayers
        Piggy Bank
        Tim Bray
        
        2005-11-01 About
        
        Stefano's Linotype ~ Piggy Bank, Cocoon and the Future of the Web
        
        Tags:
        
        Cocoon
        Mozilla
        Piggy Bank
        SIMILE
        Stefano Mazzocchi
        
        At the heart of Piggy Bank, there is a web server running inside your web browser. It's running a servlet, a minimal RESTful framework... <br/> When people ask me what I do for a living, I say that I research what the web of the future could be. At that point, they ask me to give them an example of what that would mean for them. My usual reply is "if we are successful, the only difference you'll perceive is that you won't feel as constantly lost as you feel today". At that point they smile, happy to meet a technologist who thinks it's his fault, not theirs, if they can't do something with his software.
        
        2005-10-27 About
      - SIMILE Exhibit
        
        WWW Conference Exhibits
        
        Tags:
        
        SIMILE Exhibit
        WWW 2007
        
        Exhibit based view of the WWW2007 paper
        
        2007-05-24 About
        
        AI3: Converting ‘Sweet Tools’ to an Exhibit
        
        Tags:
        
        Howto
        SIMILE Exhibit
        
        2007-01-24 About
        
        AI3’s Comprehensive Listing of Semantic Web and Related Tools
        
        Tags:
        
        Semantic Web : Tools
        SIMILE Exhibit
        
        as a SIMILE exhibit
        
        2007-01-24 About
        
        SIMILE | Exhibit
        
        Tags:
        
        SIMILE Exhibit
        
        Exhibit is a lightweight structured data publishing framework that lets you create web pages with support for sorting, filtering, and rich visualizations by writing only HTML and optionally some CSS and Javascript code.
        
        2006-12-11 About
      - SIMILE Timeline
        
        timemap - Javascript library to help use a SIMILE timeline with online maps including Google, OpenLayers, and Bing. - Google Project Hosting
        
        Tags:
        
        Google Maps
        SIMILE Timeline
        
        2012-11-28 About
        
        SIMILE Widgets | Timeline
        
        Tags:
        
        SIMILE Timeline
        
        2012-11-28 About
        
        Hey LazyWeb! : Live Clipboard and Timeline
        
        Tags:
        
        Danny Ayers
        Live Clipboard
        SIMILE Timeline
        
        2006-07-12 About
        
        SIMILE | Timeline
        
        Tags:
        
        Good
        SIMILE Timeline
        
        Timeline is a DHTML-based AJAXy widget for visualizing time-based events.
        
        2006-07-05 About
  - Princeton
    - NLP@Princeton
      - Sanjeev Arora
        A La Carte Embedding
        
        A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (2018)
        
        Tags:
        
        A La Carte Embedding
        
        2018-09-18 About
        
        Simple and efficient semantic embeddings for rare words, n-grams, and language features – Off the convex path
        
        Tags:
        
        A La Carte Embedding
        Good
        N-grams
        Rare words (NLP)
        Sanjeev Arora
        Sentence Embeddings
        Word embeddings
        
        A La Carte embeddings > Distributional methods for capturing meaning, such as word embeddings, often require observing many examples of words in context. But most humans can infer a reasonable meaning from very few or even a single occurrence... > > “Porgies live in shallow temperate marine waters” > > Inducing word embedding from their contexts: a surprising linear relationship
        
        2018-09-18 About
        
        SIF embeddings
        
        A Self-Training Approach for Short Text Clustering - (Hadifar 2019)
        
        Tags:
        
        Short Text Clustering
        SIF embeddings
        
        > The method we propose, learns discriminative features from both an autoencoder and a sentence embedding ([SIF embeddings](tag:sif_embeddings)), then uses assignments from a clustering algorithm as supervision to update weights of the encoder network.
        
        2021-05-20 About
        
        [1703.02507] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
        
        Tags:
        
        Arxiv Doc
        Sent2Vec
        SIF embeddings
        
        2019-03-25 About
        
        nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks
        
        Tags:
        
        NLP sample code
        Sentence Similarity
        SIF embeddings
        Word Mover’s Distance
        Yves Peirsman
        
        [blog post](/doc/?uri=http%3A%2F%2Fnlp.town%2Fblog%2Fsentence-similarity%2F)
        
        2019-02-07 About
        
        Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline (Ethayarajh 2018)
        
        Tags:
        
        Random walk
        Sentence Embeddings
        SIF embeddings
        
        > we show that word vector length has a confounding effect on the probability of a sentence being generated in Arora et al.’s model ([SIF embeddings](tag:sif_embeddings)). We propose a random walk model that is robust to this confound... Our approach beats Arora et al.’s by up to 44.4% on textual similarity tasks... Unlike Arora et al.’s method, ours requires no hyperparameter tuning [Github](https://github.com/kawine/usif)
        
        2018-10-08 About
        
        Deep-learning-free Text and Sentence Embedding, Part 2 – Off the convex path
        
        Tags:
        
        Sanjeev Arora
        Sentence Embeddings
        SIF embeddings
        
        > Can we design a text embedding with the simplicity and transparency of SIF while also incorporating word order information? yes we can.
        
        2018-06-25 About
        
        Deep-learning-free Text and Sentence Embedding, Part 1 – Off the convex path
        
        Tags:
        
        Sanjeev Arora
        Sentence Embeddings
        SIF embeddings
        
        > introduction to extremely simple ways of computing sentence embeddings, which on many standard tasks, beat many state-of-the-art deep learning methods. Related to [this paper](/doc/?uri=https%3A%2F%2Fopenreview.net%2Fforum%3Fid%3DSyK00v5xx) (BTW, contains a good intro to text embeddings)
        
        2018-06-25 About
        
        A Simple but Tough-to-Beat Baseline for Sentence Embeddings (2017)
        
        Tags:
        
        Good
        Sanjeev Arora
        Sentence Embeddings
        SIF embeddings
        Singular Value Decomposition
        
        > Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA/SVD [github project](https://github.com/PrincetonML/SIF) [blog post](/doc/?uri=http%3A%2F%2Fwww.offconvex.org%2F2018%2F06%2F17%2Ftextembeddings%2F) See also [youtube: Sanjeev Arora on "A theoretical approach to semantic representations"](https://www.youtube.com/watch?v=KR46z_V0BVw)
        
        2018-05-10 About
        
        [2307.15936] A Theory for Emergence of Complex Skills in Language Models
        
        Tags:
        
        Arxiv Doc
        Emergence in LLMs
        NLP@Google
        Sanjeev Arora
        
        [New Theory Suggests Chatbots Can Understand Text | Quanta Magazine](doc:2024/02/new_theory_suggests_chatbots_ca)
        
        2024-02-24 About
        
        Sanjeev Arora sur X : "It's better to use just 5% of the instruction-tuning data (suitably selected) instead of the full dataset."
        
        Tags:
        
        Instruction tuning
        Sanjeev Arora
        
        2024-02-15 About
        
        New Theory Suggests Chatbots Can Understand Text | Quanta Magazine
        
        Tags:
        
        Emergence in LLMs
        Geoffrey Hinton
        Sanjeev Arora
        Sebastien Bubeck
        Stochastic Parrots
        
        Article on (i) theory of emergence of complex skills in LLMs (ii) SKILL-MIX eval -- shows LLMs able to use skills combos not seen during training. ([Arora](tag:sanjeev_arora)) > “Stochastic parrots” generate text only by combining information they have already seen, not through any understanding of their own. Are ChatGPT, Bard and other large chatbots simply parroting their training data? The answer is probably no. [[2307.15936] A Theory for Emergence of Complex Skills in Language Models](doc:2024/02/2307_15936_a_theory_for_emerg)
        
        2024-02-11 About
        
        [2302.06600] Task-Specific Skill Localization in Fine-tuned Language Models
        
        Tags:
        
        Arxiv Doc
        LM Fine-tuning
        Parameter-efficient fine-tuning (PEFT)
        Sanjeev Arora
        
        [tweet thread](https://twitter.com/Abhishek_034/status/1676914445442850816): > Fine-tuning LLM on a task gives it new skill. Our “Skill localization” paper shows this skill lives in < 0.01% parameters — rest can be reverted to pre-trained values. > Given a pre-trained (PT) model and a fine-tuned (FT) model, we learn a binary mask of 0.01% sparsity on the FT model parameters such that grafting the FT values for just the activated subset of parameters onto the pre-trained model recovers >95% of the FT model’s performance. > In multi-task settings, overlap of grafts gives a notion of task similarity. > Different from lottery ticket -- Localization done post-hoc w/ no re-training. Different from parameter-efficient FT methods that restrict training to a few parameters. [GitHub](https://github.com/abhishekpanigrahi1996/Skill-Localization-by-grafting)
        
        2023-08-25 About
        
        Sanjeev Arora sur Twitter : "new `skills' induced by LLM fine-tuning can be localized in tiny fraction of the model."
        
        Tags:
        
        Continual Learning
        LM Fine-tuning
        Neural network interpretability
        Sanjeev Arora
        Tweet
        
        > Excited about implication for continual learning, interpretability etc.
        
        2023-07-07 About
        
        Sanjeev Arora sur Twitter : "Fine-tuning language models using just forward pass!...r
        
        Tags:
        
        Backpropagation
        LM Fine-tuning
        Sanjeev Arora
        Tweet
        
        2023-06-09 About
        
        ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation
        
        Tags:
        
        Foundation Models
        Kigali
        Lenka Zdeborová
        Sanjeev Arora
        Workshop
        
        2023-05-04 About
        
        Sanjeev Arora sur Twitter : "A priori, fine-tuning a huge LM on a few datapoints could lead to catastrophic overfitting. So why doesn’t it? Our theory + experiments..."
        
        Tags:
        
        LM Fine-tuning
        Sanjeev Arora
        Tweet
        
        2022-10-14 About
        
        [2202.14037] Understanding Contrastive Learning Requires Incorporating Inductive Biases
        
        Tags:
        
        Arxiv Doc
        Contrastive Learning
        Sanjeev Arora
        
        2022-03-05 About
        
        How to allow deep learning on your data without revealing the data – Off the convex path
        
        Tags:
        
        Deep Learning
        Encryption
        Sanjeev Arora
        
        > Our InstaHide allows users and IoT devices to "encrypt" data yet allowing deep learning on it. Minor efficiency and accuracy loss. Carlini et al broke our 100-image challenge dataset in 12 too 120 GPU hrs. Does it invalidate intended use? [src](https://twitter.com/prfsanjeevarora/status/1326653490261843968?s=20) [follow up](https://twitter.com/prfsanjeevarora/status/1326653490261843968?s=20) - "[Brief response to Nicholas Carlini’s blog post](https://hazelsuko07.github.io/Response_to_carlini_blogpost/)"
        
        2020-11-12 About
        
        [1902.09229] A Theoretical Analysis of Contrastive Unsupervised Representation Learning
        
        Tags:
        
        Arxiv Doc
        Embeddings
        Noise contrastive estimation
        Sanjeev Arora
        
        [blog post](/doc/?uri=http%3A%2F%2Fwww.offconvex.org%2F2019%2F03%2F19%2FCURL%2F)
        
        2019-03-20 About
        
        Contrastive Unsupervised Learning of Semantic Representations: A Theoretical Framework – Off the convex path (2019-03)
        
        Tags:
        
        Contrastive Self-Supervised Learning
        Embeddings
        Sanjeev Arora
        Word2vec
        
        [paper](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1902.09229). Why do objectives similar the one used by word2vec succeed in such diverse settings? ("Contrastive Unsupervised Representation Learning" (CURL): **methods that leverage similar pairs of data points**) > In contrastive learning the objective used at test time is very different from the training objective: generalization error is not the right way to think about this. -> a framework that formalizes the notion of semantic similarity that is implicitly used by these algorithms > **if the unsupervised loss happens to be small at the end of contrastive learning then the resulting representations perform well on downstream classification**
        
        2019-03-20 About
        
        Word Embeddings: Explaining their properties – Off the convex path (2016)
        
        Tags:
        
        Sanjeev Arora
        Word embeddings
        
        second part for [this post](/doc/?uri=http%3A%2F%2Fwww.offconvex.org%2F2015%2F12%2F12%2Fword-embeddings-1%2F) >- What properties of natural languages cause these low-dimensional embeddings to exist? >- Why do low-dimensional embeddings work better at analogy solving than high dimensional embeddings? >- Why do Semantic Relations correspond to Directions?
        
        2019-03-20 About
        
        Linear algebraic structure of word meanings – Off the convex path
        
        Tags:
        
        Sanjeev Arora
        Sparse coding
        Word embeddings
        Word sense / Lexical ambiguity
        
        Extracting word senses from embeddings. [About this paper](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1601.03764)
        
        2018-09-20 About
        
        Simple and efficient semantic embeddings for rare words, n-grams, and language features – Off the convex path
        
        Tags:
        
        A La Carte Embedding
        Good
        N-grams
        Rare words (NLP)
        Sanjeev Arora
        Sentence Embeddings
        Word embeddings
        
        A La Carte embeddings > Distributional methods for capturing meaning, such as word embeddings, often require observing many examples of words in context. But most humans can infer a reasonable meaning from very few or even a single occurrence... > > “Porgies live in shallow temperate marine waters” > > Inducing word embedding from their contexts: a surprising linear relationship
        
        2018-09-18 About
        
        Off the convex path
        
        Tags:
        
        ML/NLP blog
        Sanjeev Arora
        
        2018-09-09 About
        
        A Latent Variable Model Approach to PMI-based Word Embeddings (2016)
        
        Tags:
        
        Sanjeev Arora
        Word embeddings
        
        [Related YouTube video](/doc/?uri=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DKR46z_V0BVw) Based on a generative model (random walk on words involving a latent discourse vector), a rigorous justification for models such as word2vec and GloVe, including the hyperparameter choices for the latter, and a mathematical explanation for why these word embeddings allow analogies to be solved using linear algebra.
        
        2018-08-28 About
        
        [1601.03764] Linear Algebraic Structure of Word Senses, with Applications to Polysemy
        
        Tags:
        
        Arxiv Doc
        EMNLP 2018
        Sanjeev Arora
        Sparse coding
        Word embeddings
        Word sense / Lexical ambiguity
        
        > Here it is shown that multiple word senses reside in linear superposition within the word embedding and simple sparse coding can recover vectors that approximately capture the senses > Each extracted word sense is accompanied by one of about 2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense. > The success of the approach is mathematically explained using a variant of the random walk on discourses model ("random walk": a generative model for language). Under the assumptions of this model, there exists a linear relationship between the vector of a word w and the vectors of the words in its contexts (It is not the average of the words in w's context, but in a given corpus the matrix of the linear relationship does not depend on w. It can be estimated, and so we can compute the embedding of a word from the contexts it belongs to) [Related blog post](/doc/?uri=https%3A%2F%2Fwww.offconvex.org%2F2016%2F07%2F10%2Fembeddingspolysemy%2F)
        
        2018-08-28 About
        
        Mathematics of Machine Learning: An introduction
        
        Tags:
        
        Sanjeev Arora
        
        2018-08-08 About
        
        Mathematics of Machine Learning and Deep Learning - Plenary talk at International Congress of Mathematicians 2018
        
        Tags:
        
        Sanjeev Arora
        
        [article](/doc/?uri=https%3A%2F%2Fwww.dropbox.com%2Fs%2Fy59petiffzq63gt%2Fmain.pdf%3Fdl%3D0)
        
        2018-08-08 About
        
        Deep-learning-free Text and Sentence Embedding, Part 2 – Off the convex path
        
        Tags:
        
        Sanjeev Arora
        Sentence Embeddings
        SIF embeddings
        
        > Can we design a text embedding with the simplicity and transparency of SIF while also incorporating word order information? yes we can.
        
        2018-06-25 About
        
        Deep-learning-free Text and Sentence Embedding, Part 1 – Off the convex path
        
        Tags:
        
        Sanjeev Arora
        Sentence Embeddings
        SIF embeddings
        
        > introduction to extremely simple ways of computing sentence embeddings, which on many standard tasks, beat many state-of-the-art deep learning methods. Related to [this paper](/doc/?uri=https%3A%2F%2Fopenreview.net%2Fforum%3Fid%3DSyK00v5xx) (BTW, contains a good intro to text embeddings)
        
        2018-06-25 About
        
        Sanjeev Arora on "A theoretical approach to semantic representations" - YouTube (2016)
        
        Tags:
        
        Sanjeev Arora
        Word embeddings
        YouTube video
        
        Why do low-dimensional word vectors exist? > a text corpus is imagined as being generated by a random walk in a latent variable space, and the word production is via a loglinear distribution. This model is shown to imply several empirically discovered past methods for word embedding like word2vec, GloVe, PMI etc [Related paper](/doc/?uri=http%3A%2F%2Fwww.aclweb.org%2Fanthology%2FQ16-1028)
        
        2018-06-10 About
        
        A Theoretical Approach to Semantic Coding and Hashing | Simons Institute for the Theory of Computing (2016)
        
        Tags:
        
        Good
        Sanjeev Arora
        Semantic Hashing
        Word embeddings
        
        2018-05-26 About
        
        A Simple but Tough-to-Beat Baseline for Sentence Embeddings (2017)
        
        Tags:
        
        Good
        Sanjeev Arora
        Sentence Embeddings
        SIF embeddings
        Singular Value Decomposition
        
        > Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA/SVD [github project](https://github.com/PrincetonML/SIF) [blog post](/doc/?uri=http%3A%2F%2Fwww.offconvex.org%2F2018%2F06%2F17%2Ftextembeddings%2F) See also [youtube: Sanjeev Arora on "A theoretical approach to semantic representations"](https://www.youtube.com/watch?v=KR46z_V0BVw)
        
        2018-05-10 About
        
        Semantic Word Embeddings – Off the convex path
        
        Tags:
        
        Good
        Sanjeev Arora
        Word embeddings
        
        ([Part 2](/doc/?uri=http%3A%2F%2Fwww.offconvex.org%2F2016%2F02%2F14%2Fword-embeddings-2%2F))
        
        2017-11-21 About
      - Wordnet
        
        RDF/OWL Representation of WordNet
        
        Tags:
        
        Wordnet
        
        This document presents a standard conversion of Princeton WordNet to RDF/OWL. It describes how it was converted and gives examples of how it may be queried for use in Semantic Web applications.
        
        2006-06-19 About
    - Wordnet
  - Stanford
    - AI@Stanford
      - NLP@Stanford
        Alpaca
        
        How to use Alpaca-LoRA to fine-tune a model like ChatGPT
        
        Tags:
        
        Alpaca
        ChatGPT
        ChatGPT fine-tuning
        LoRA
        
        2023-05-26 About
        
        Daniel Vila Suero sur Twitter : "Data quality is key for LLMs, but we're building Open Source LLMs with data of "unknown" quality... Introducing Alpaca GarbageCollector..."
        
        Tags:
        
        Alpaca
        Dataset quality
        SetFit (SBERT fine-tuning)
        Tweet
        
        > a cross-lingual SetFit model to identify potential bad instructions in Alpaca-like datasets
        
        2023-04-05 About
        
        Alpaca Finetuning of Llama on a 24G Consumer GPU
        
        Tags:
        
        Alpaca
        Fine-tuning
        
        [GitHub](https://github.com/aspctu/alpaca-lora) fork of [tloen/alpaca-lora: Instruct-tune LLaMA on consumer hardware](doc:2023/03/tloen_alpaca_lora_instruct_tun)
        
        2023-03-27 About
        
        tloen/alpaca-lora: Instruct-tune LLaMA on consumer hardware
        
        Tags:
        
        Alpaca
        GitHub project
        GPT: frugal alternatives
        LLaMA
        
        Uses [LoRA: Low-Rank Adaptation of Large Language Models](doc:2023/03/2106_09685_lora_low_rank_ada) see [Alpaca Finetuning of Llama on a 24G Consumer GPU](doc:2023/03/alpaca_finetuning_of_llama_on_a)
        
        2023-03-22 About
        
        LLM Zoo at Home: LLaMA & Alpaca | bergis universe of software, hardware and ideas
        
        Tags:
        
        Alpaca
        LLaMA
        
        2023-03-20 About
        
        ColBERT
        
        [2404.13950] SPLATE: Sparse Late Interaction Retrieval
        
        Tags:
        
        Arxiv Doc
        ColBERT
        Sparse retrieval
        SPLADE
        
        Adapts the ColBERTv2 model to map its embeddings to a sparse space, enabling efficient sparse retrieval for candidate generation in the late interaction paradigm.
        
        2024-04-23 About
        
        ColBERT gist:c1182551fa609736d47df4af82f7c5ab
        
        Tags:
        
        ColBERT
        Sample code
        
        > a quick gist that does synthetic data gen, fine-tuning, eval. Just add your own documents, or try it on a PG essay. @JoshPurtell
        
        2024-03-08 About
        
        ColBERT Inference in the Browser
        
        Tags:
        
        ColBERT
        Demo
        Vespa
        
        Demo of ColBERT query-passage scoring interpretability - try with the following: "what are the mentioned EICPS?" and passage "There is a security risk related to EICPS 67" - MaxSim Score: 20.71 - Estimated Relevance: 64.71% - highlights: There related - then "what are the mentioned animals?" and "There is a security risk related to lions" - MaxSim Score: 9.18 - Estimated Relevance: 28.68% - highlights: related lions ``` Effects of climate change on marine ecosystems MaxSim Score: 27.90 Estimated Relevance: 87.17% Effects of global warming on marine ecosystems MaxSim Score: 24.62 Estimated Relevance: 76.94% Effects of global warming on life in the oceans MaxSim Score: 19.64 Estimated Relevance: 61.39% Effects of global warming on life on Mars MaxSim Score: 13.65 Estimated Relevance: 42.65% ```
        
        2024-03-08 About
        
        Announcing Vespa Long-Context ColBERT | Vespa Blog
        
        Tags:
        
        @jobergum
        ColBERT
        Long documents
        Vespa
        
        2024-03-03 About
        
        Omar Khattab sur X : "ColBERT in 81 languages by generalizing from English training! ..."
        
        Tags:
        
        ColBERT
        Multilingual NLP
        Omar Khattab
        Tweet
        
        2024-02-28 About
        
        Omar Khattab sur X : "A thread on late interaction, how it works efficiently, and why/where it's been shown to improve quality"
        
        Tags:
        
        ColBERT
        Omar Khattab
        Twitter thread
        
        2024-02-05 About
        
        Craig Macdonald sur X : "a thread of our main ColBERT research findings"
        
        Tags:
        
        ColBERT
        Twitter thread
        
        2024-02-01 About
        
        long document ranking · Issue #11 · stanford-futuredata/ColBERT
        
        Tags:
        
        ColBERT
        Long documents
        
        [Instructions on using ColBERT](https://github.com/stanford-futuredata/ColBERT/issues/73) passage and query length, etc
        
        2024-01-31 About
        
        Exploring ColBERT with RAGatouille | Simon Willison’s TILs
        
        Tags:
        
        ColBERT
        RAGatouille
        
        2024-01-29 About
        
        Jo Kristian Bergum sur X : (on "why using ColBERT?")
        
        Tags:
        
        @jobergum
        ColBERT
        Twitter thread
        
        > The idea that you can accurately boil down the nuances of ~256 tokens (2/3rds of a page) into a single vector is a pretty wild proposition > Inspired by @lateinteraction , we hacked into the ColBERT model's contextualized late-interaction similarities to produce [interpretable snippets](https://x.com/jobergum/status/1750282246072746178?s=20)!
        
        2024-01-28 About
        
        bclavie/RAGatouille
        
        Tags:
        
        Benjamin Clavié
        ColBERT
        GitHub project
        RAG (Retrieval-Augmented Generation)
        RAGatouille
        
        > RAGatouille's purpose is make it easy to use state-of-the-art methods in your RAG pipeline, without having to worry about the details or the years of literature! At the moment, RAGatouille focuses on making ColBERT simple to use. [Using ColBERT in-memory: Index-Free Encodings & Search](https://github.com/bclavie/RAGatouille/blob/0.0.5b1/examples/06-index_free_use.ipynb) ``` from ragatouille import RAGPretrainedModel RAG = RAGPretrainedModel.from_pretrained "colbert-ir/colbertv2. 0" ) # Your documents, a plain old list of chunked strings. documents = [...] # In-memory indexing supports metadata too! meta = ['attribute': ' really cool value'}...] # All the magic happens here RAG.encode documents, document_metadatas=meta) # Query your in-memory index RAG. search_encoded_docs(query = "A great question", k=3) # All further encode() calls add to the existing documents... RAG.encode(extra_documents, document_metadatas=extra_meta) # ... until you clear them RAG.clear_encoded ```
        
        2024-01-26 About
        
        Omar Khattab sur X : "A🧵on beating the hardware lottery for retrieval: the internals of the late interaction stack. ColBERT..."
        
        Tags:
        
        ColBERT
        Omar Khattab
        Twitter thread
        
        2023-12-29 About
        
        raphaelsty/neural-cherche: Neural Search
        
        Tags:
        
        Cherche (Raphaël)
        ColBERT
        GitHub project
        Neural Search
        Raphaël Sourty
        SPLADE
        
        > a library to fine-tune neural search models such as Splade, ColBERT, and SparseEmbed on a specific dataset
        
        2023-11-17 About
        
        Omar Khattab sur X : "This isn't the main point of this great new paper by @sophiaalthammer et al. But it's incredible how ColBERT at 1000 training queries is better than DPR trained at *50,000* queries!"
        
        Tags:
        
        ColBERT
        Omar Khattab
        Tweet
        
        mentions [Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection](doc:2023/09/2309_06131_annotating_data_fo) > why is ColBERT so data-efficient? > Answer: > > - ColBERT neither needs to learn how to condense each document (unlike DPR) > - nor how to do matching (unlike MonoBERT). > - Just needs to learn contextual term representations—a much lower burden on the encoders.
        
        2023-09-14 About
        
        LangChain Retrieval Webinar - YouTube
        
        Tags:
        
        ColBERT
        DSPy (Demonstrate-Search-Predict)
        LangChain
        Omar Khattab
        Webinar
        YouTube video
        
        [ColBERT](tag:colbert) retrieval model and the [DSP](doc:2023/02/stanfordnlp_dsp_𝗗𝗦𝗣_demons) programming model
        
        2023-05-27 About
        
        [2304.01982] Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
        
        Tags:
        
        Arxiv Doc
        ColBERT
        Multi-Vector Retrieval
        Neural Search
        NLP@Google
        
        > Multi-vector retrievers like [ColBERT](tag:colbert) are powerful, but they come at the cost of complicated inference. In this paper, we ask: "can token retrieval alone achieve great performance in multi-vector retrieval?" [tweet](https://twitter.com/leejnhk/status/1643632578824396805?s=20) > The key insight of XTR is that the token-retrieval in multi-vector models should be **trained to retrieve the most salient and informative document tokens**, so that the score between a query and document can be computed using only the retrieved information, just like how single-vector retrieval models work > This is an *amazing* way to re-engineer the scoring mechanism of late interaction / ColBERT retrievers! [src: ColBERT's author Omar Khattab](https://twitter.com/lateinteraction/status/1643439889902637056?s=20) - scoring using only retrieved document terms - imputing missing token scores using their upper bound
        
        2023-04-05 About
        
        Omar Khattab sur Twitter : "New features in 𝗖𝗼𝗹𝗕𝗘𝗥𝗧 ‣ Index Updater. Simple API to add/remove documents..."
        
        Tags:
        
        ColBERT
        Tweet
        
        2023-01-30 About
        
        [2203.13088] Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
        
        Tags:
        
        Arxiv Doc
        ColBERT
        
        [tweet](https://twitter.com/s_hofstaetter/status/1508803785317138435)
        
        2022-03-30 About
        
        Integrate ORQA and REALM for Open Domain Question Answering · Issue #312 · deepset-ai/haystack
        
        Tags:
        
        ColBERT
        Haystack
        REALM
        
        mntions [[2002.08909] REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
        
        2022-01-12 About
        
        [2004.12832] ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
        
        Tags:
        
        Arxiv Doc
        ColBERT
        
        (The 1st Colbert paper)
        
        2022-01-12 About
        
        [2007.00814] Relevance-guided Supervision for OpenQA with ColBERT
        
        Tags:
        
        Arxiv Doc
        ColBERT
        
        Colbert-QA
        
        2022-01-07 About
        
        [2112.01488] ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
        
        Tags:
        
        Arxiv Doc
        ColBERT
        Domain specific corpora
        
        2021-12-05 About
        
        Building Scalable, Explainable, and Adaptive NLP Models with Retrieval | SAIL Blog
        
        Tags:
        
        ColBERT
        Good
        NLP@Stanford
        Not Encoding Factual Knowledge in Language Model
        Retrieval augmented LM
        Retrieval-based NLP
        
        > The black-box nature of large language models like T5 and GPT-3 makes them inefficient to train and deploy, opaque in their knowledge representations and in backing their claims with provenance, and static in facing a constantly evolving world and diverse downstream contexts. **This post explores retrieval-based NLP, where models retrieve information pertinent to solving their tasks from a plugged-in text corpus**. > > Retrieval-based NLP methods view tasks as “open-book” exams: knowledge is encoded explicitly in the form of a text corpus like Wikipedia, the medical literature, or a software’s API documentation. When solving a language task, **the model learns to search for pertinent passages** and to then use the retrieved information for crafting knowledgeable responses. In doing so, **retrieval helps decouple the capacity that language models have for understanding text from how they store knowledge**
        
        2021-10-07 About
        
        A Moderate Proposal for Radically Better AI-powered Web Search
        
        Tags:
        
        ColBERT
        Neural Search
        NLP@Stanford
        Web search
        
        2021-07-10 About
        
        Coursera: NLP class
        
        Introduction to Information Retrieval, Cambridge University Press (2008) Manning, Raghavan, and Schütze
        
        Tags:
        
        AI: books & journals
        Chris Manning
        Coursera: NLP class
        Good
        Information retrieval
        Information retrieval: techniques
        NLP@Stanford
        
        2012-04-01 About
        
        SRILM - The SRI Language Modeling Toolkit
        
        Tags:
        
        Coursera: NLP class
        Language Model
        NLP tools
        
        SRILM is a toolkit for building and applying statistical language models (LMs), primarily for use in speech recognition, statistical tagging and segmentation, and machine translation.
        
        2012-03-24 About
        
        Unix for Poets
        
        Tags:
        
        Coursera: NLP class
        Unix
        
        2012-03-19 About
        
        Porter Stemming Algorithm
        
        Tags:
        
        Coursera: NLP class
        Stemming
        
        2012-03-18 About
        
        Regular-Expressions.info - Regex Tutorial, Examples and Reference - Regexp Patterns
        
        Tags:
        
        Coursera: NLP class
        Regex
        
        2012-03-07 About
        
        cs224n
        
        Prompting, Instruction Finetuning, and RLHF (CS224N)
        
        Tags:
        
        ChatGPT
        ChatGPT: training
        cs224n
        Instruction tuning
        Prompting/In-context learning
        RL from Human Feedback
        Slides
        
        2023-02-16 About
        
        cs224n Lecture 13: Integrating Knowledge in Language Models
        
        Tags:
        
        cs224n
        Language Models + Knowledge
        Slides
        
        2022-07-08 About
        
        Softmax classifier (CS231n Convolutional Neural Networks for Visual Recognition)
        
        Tags:
        
        Andrej Karpathy
        Cross-Entropy
        cs224n
        NLP@Stanford
        Softmax
        
        > the cross-entropy objective wants the predicted distribution to have all of its mass on the correct answer.
        
        2020-06-04 About
        
        CS224n: Natural Language Processing with Deep Learning Stanford / Winter 2019
        
        Tags:
        
        cs224n
        NLP@Stanford
        
        2019-09-30 About
        
        CS224n: Natural Language Processing with Deep Learning
        
        Tags:
        
        cs224n
        NLP@Stanford
        
        2019-04-03 About
        
        GloVe
        
        I.A.B sur Twitter : "When we "know the meaning" of a word, what is it that we know? For example, what does knowing the words "dolphin" and "tiger" entail?..."
        
        Tags:
        
        GloVe
        Meaning in NLP
        Tweet
        Word embeddings
        
        2022-04-15 About
        
        Pablo Castro sur Twitter : "Random finding of the day for word embeddings: vec("apple")-vec("apples") yields a vector close to ipad, ipod, etc. (apples removes the "fruitness" from apple)
        
        Tags:
        
        Fun fact
        GloVe
        Tweet
        Word embeddings
        
        2020-12-18 About
        
        An overview of word embeddings and their connection to distributional semantic models - AYLIEN (2016)
        
        Tags:
        
        Distributional semantics
        GloVe
        Good
        Sebastian Ruder
        Word2vec
        Word embedding: evaluation
        Word embeddings
        
        > While on the surface DSMs and word embedding models use varying algorithms to learn word representations – the former count, the latter predict – both types of model fundamentally act on the same underlying statistics of the data, i.e. the co-occurrence counts between words... > These results are in contrast to the general consensus that word embeddings are superior to traditional methods. Rather, they indicate that it typically makes no difference whatsoever whether word embeddings or distributional methods are used. What really matters is that your hyperparameters are tuned and that you utilize the appropriate pre-processing and post-processing steps.
        
        2017-07-20 About
        
        Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014)
        
        Tags:
        
        GloVe
        Word2vec
        Word embedding: evaluation
        Word embeddings
        
        (good presentation in the intro of context-counting vs. context-predicting vectors)
        
        2017-05-18 About
        
        How is GloVe different from word2vec? - Quora
        
        Tags:
        
        GloVe
        Word2vec
        Word embeddings
        
        Both learn geometrical encodings (vectors) of words from their co-occurrence information. Word2vec is a "predictive" model, whereas GloVe is a "count-based" model.
        
        2017-05-18 About
        
        GloVe: Global Vectors for Word Representation
        
        Tags:
        
        Chris Manning
        GloVe
        NLP@Stanford
        Richard Socher
        
        2017-05-18 About
        
        Stanford classifier
        
        java - Method(s) to output confidence score from Stanford Classifier? - Stack Overflow
        
        Tags:
        
        Stack Overflow
        Stanford classifier
        
        2014-04-08 About
        
        [java-nlp-user] Stanford NER: confidence scores
        
        Tags:
        
        Stanford classifier
        
        2014-04-08 About
        
        The Stanford classifier
        
        Tags:
        
        Stanford classifier
        
        2014-03-16 About
        
        Stanford NER
        
        Named Entity Recognition and the Stanford NER Software (slides)
        
        Tags:
        
        Slides
        Stanford NER
        
        2018-05-20 About
        
        Stanford Named Entity Recognizer
        
        Tags:
        
        Stanford NER
        
        general implementation of (arbitrary order) linear chain Conditional Random Field (CRF) sequence models
        
        2018-05-20 About
        
        How to Train your Own Model with NLTK and Stanford NER Tagger? (for English, French, German…)
        
        Tags:
        
        NLTK
        Stanford NER
        
        2018-05-20 About
        
        Stanford POS Tagger
        
        dicksontsai/stanford-nlp-local-extension: Chrome extension for sending content to localhost server running Stanford NLP tools.
        
        Tags:
        
        Chrome extension
        GitHub project
        Sample code
        Servlet
        Stanford POS Tagger
        
        2020-07-03 About
        
        Dive Into NLTK, Part V: Using Stanford Text Analysis Tools in Python – Text Mining Online
        
        Tags:
        
        Java in python
        NLTK
        Stanford POS Tagger
        
        [en cas de pb](https://gist.github.com/alvations/e1df0ba227e542955a8a) **including how to use Java NLP Tools in python** ``` export CLASSPATH=/Users/fps/_fps/DeveloperTools/stanford-postagger-full/stanford-postagger.jar # ATTENTION, stanford-postagger.jar, pas stanford-postagger-3.8.0.jar export STANFORD_MODELS=/Users/fps/_fps/DeveloperTools/stanford-postagger-full/models python ``` ``` from nltk.tag import StanfordPOSTagger st = StanfordPOSTagger('english-bidirectional-distsim.tagger') st.tag('What is the airspeed of an unladen swallow ?'.split()) st = StanfordPOSTagger('french.tagger') st.tag('Les plats servis sont toujours les mêmes et la qualité des plats est en nette baisse'.split()) ``` [('Les', 'DET'), ('plats', 'NOUN'), ('servis', 'ADJ'), ('sont', 'VERB'), ('toujours', 'ADV'), ('les', 'DET'), ('mêmes', 'ADJ'), ('et', 'CONJ'), ('la', 'DET'), ('qualité', 'NOUN'), ('des', 'DET'), ('plats', 'NOUN'), ('est', 'VERB'), ('en', 'ADP'), ('nette', 'ADJ'), ('baisse', 'NOUN')]
        
        2017-07-11 About
        
        NLTK: Installing Third Party Software · nltk Wiki
        
        Tags:
        
        NLTK
        Stanford POS Tagger
        
        2017-07-11 About
        
        Source code for nltk.tag.stanford — NLTK documentation
        
        Tags:
        
        NLTK
        Stanford POS Tagger
        
        2017-07-11 About
        
        nltk.tag.stanford module — NLTK documentation
        
        Tags:
        
        NLTK
        Stanford POS Tagger
        
        A module for interfacing with the Stanford taggers.
        
        2017-07-11 About
        
        Stanford Log-linear Part-Of-Speech Tagger
        
        Tags:
        
        Stanford POS Tagger
        
        2017-07-11 About
        
        Stanza
        
        stanfordnlp/stanza: Official Stanford NLP Python Library for Many Human Languages
        
        Tags:
        
        NLP@Stanford
        Python library
        Stanza
        
        2021-09-20 About
        
        [2404.03592] ReFT: Representation Finetuning for Language Models
        
        Tags:
        
        Arxiv Doc
        Chris Manning
        Dan Jurafsky
        NLP@Stanford
        Parameter-efficient fine-tuning (PEFT)
        
        > ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations
        
        2024-04-08 About
        
        [2401.18059] RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
        
        Tags:
        
        Arxiv Doc
        Chris Manning
        Information retrieval
        Long documents
        NLP@Stanford
        RAPTOR
        Tree structure
        
        > most existing RAG methods retrieve only short contiguous chunks from a retrieval corpus, limiting holistic understanding of the overall document context. > RAPTOR recursively clusters chunks of text based on their vector embeddings and generates text summaries of those clusters, constructing a tree from the bottom up. > Our main contribution is the idea of using text summarization to allow retrieval augmentation of context at different scales
        
        2024-02-03 About
        
        Zain Hasan sur X : "why do large language models pay more attention to and reason better over the beginning and end of what you tell them in prompts?"
        
        Tags:
        
        LMs: context length
        NLP@Stanford
        Prompts
        Tweet
        
        2023-11-19 About
        
        Designing Natural Language Processing Tools for Teachers
        
        Tags:
        
        NLP@Stanford
        NLP + education
        
        2023-10-24 About
        
        Archit Sharma sur Twitter : "Direct Preference Optimization (DPO) allows you to fine-tune LMs directly from preferences via a simple classification loss, no RL required"
        
        Tags:
        
        NLP@Stanford
        RL from Human Feedback
        Tweet
        
        2023-05-31 About
        
        Eric sur Twitter : "...Introducing Direct Preference Optimization (DPO), a simple classification loss provably equivalent to RLHF"
        
        Tags:
        
        NLP@Stanford
        RL from Human Feedback
        Tweet
        
        2023-05-31 About
        
        [2304.09848] Evaluating Verifiability in Generative Search Engines
        
        Tags:
        
        Arxiv Doc
        Generative Search Engines
        LM evaluation
        NLP@Stanford
        
        > We conduct human evaluation to audit four popular generative search engines -- Bing Chat, NeevaAI, perplexity.ai, and YouChat... We find that responses from existing generative search engines are fluent and appear informative, but frequently contain unsupported statements and inaccurate citations: on average, a mere 51.5% of generated sentences are fully supported by citations and only 74.5% of citations support their associated sentence. We believe that these results are concerningly low for systems that may serve as a primary tool for information-seeking users, especially given their facade of trustworthiness.
        
        2023-04-23 About
        
        stanfordnlp/dspy: 𝗗𝗦𝗣: Demonstrate-Search-Predict. A framework for composing retrieval and language models for knowledge-intensive NLP.
        
        Tags:
        
        DSPy (Demonstrate-Search-Predict)
        GitHub project
        Knowledge-Intensive NLP Tasks
        LM + Search
        NLP@Stanford
        NLP based IR
        Omar Khattab
        Prompting/In-context learning
        Retriever-Reader
        
        (initially called DSP, rebranded as DSPy) > The DSP framework provides a programming abstraction for building grounded AI systems. In a few lines of code, a DSP program expresses rich interactions between retrieval models (RMs) and language models (LMs) to tackle difficult knowledge-intensive NLP tasks (e.g., complex question answering or conversational search). > DSP discourages ["prompt engineering"](tag:prompted_models), which we view much the same way as hyperparameter tuning in traditional ML [@matei_zaharia](https://twitter.com/matei_zaharia/status/1626705622585716737?s=20): >Who are the World Cup champions? I knew ChatGPT would get it wrong when it launched, but it's surprising that all the new search+LLM engines do too. > > **Combining retrieval+LMs won't just be a matter of prompting**. That's why we've been building tools like DSP at Stanford to do it.
        
        2023-02-18 About
        
        [2203.14465] STaR: Bootstrapping Reasoning With Reasoning
        
        Tags:
        
        Arxiv Doc
        Chain-of-thought
        LLM
        NLP@Google
        NLP@Stanford
        Reasoning
        
        "Self-Taught Reasoner" (STaR) > (to our knowledge) the first technique to allow a pre-trained large language model to iteratively use its language modeling capacity to improve itself > Generating step-by-step "chain-of-thought" rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering. However, inducing language model rationale generation currently requires either constructing massive rationale datasets or sacrificing accuracy by using only few-shot inference. We propose **a technique to iteratively leverage a small number of rationale examples and a large dataset without rationales**, to bootstrap the ability to perform successively more complex reasoning.
        
        2023-02-07 About
        
        [2301.04709] Causal Abstraction for Faithful Model Interpretation
        
        Tags:
        
        Arxiv Doc
        Confiance AI
        Explainable AI
        Explainable NLP
        LIME
        NLP@Stanford
        
        > A faithful and interpretable explanation of an AI model's behavior and internal structure is a **high-level explanation that is human-intelligible but also consistent with the known, but often opaque low-level causal details of the model**. We argue that the theory of **causal abstraction** provides the mathematical foundations for the desired kinds of model explanations > We take the fundamental question in explainable artificial intelligence (XAI) to be why a deep learning model makes the predictions it does. > XAI needs a theory for when a high-level causal explanation [that is, interpretable by humans] is harmonious with a low-level causal explanation. > A high-level (possibly symbolic) model is a faithful proxy for a low-lever (in our setting, usually neural) model when we can align high-level variables with sets of low-level variables that play the same causal role
        
        2023-01-14 About
        
        Stanford studied 30 large language models so you don’t have to
        
        Tags:
        
        Benchmark
        Language Model
        NLP@Stanford
        
        > Scholars benchmark 30 prominent language models across a wide range of scenarios and for a broad range of metrics to elucidate their capabilities and risks.
        
        2022-12-20 About
        
        shikhar sur Twitter : "Instead of asking whether tree structure should be baked into NNs, our new paper asks if transformers already have a tendency to learn tree structured computations when trained on language, and if this structure is predictive of generalization! "
        
        Tags:
        
        Generalization
        NLP@Stanford
        Transformers
        Tree structure
        Tweet
        
        2022-12-09 About
        
        [2212.01340] Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
        
        Tags:
        
        Arxiv Doc
        Benchmark
        Information retrieval
        NLP@IBM
        NLP@Stanford
        
        2022-12-06 About
        
        Stanford NLP Group sur Twitter : "YONO: You Only Need One Model for Open-domain Question Answering..."
        
        Tags:
        
        NLP@Stanford
        Open Domain Question Answering
        Retriever-Ranker
        Tweet
        
        2022-12-05 About
        
        Talking to Models: Stanford U & Microsoft Method Enables Developers to Correct Model Bugs via Natural Language Patches | Synced
        
        Tags:
        
        Chris Manning
        Fine-tuning
        Language Model
        NLP@Microsoft
        NLP@Stanford
        
        While current methods for fixing bugs in language models typically rely on brittle patches or much data for finetuning, a novel approach uses declarative statements
        
        2022-11-27 About
        
        Meet CoAuthor, an Experiment in Human-AI Collaborative Writing
        
        Tags:
        
        GPT-3
        Human-AI collaboration
        NLP@Stanford
        
        > Researchers study how humans and AI can write together by designing large interaction datasets.
        
        2022-11-13 About
        
        [2210.09338] Deep Bidirectional Language-Knowledge Graph Pretraining
        
        Tags:
        
        Arxiv Doc
        Chris Manning
        Jure Leskovec
        Knowledge Graphs and NLP
        LM + Link Prediction
        NLP@Stanford
        
        > DRAGON (Deep Bidirectional Language-Knowledge Graph Pretraining), a self-supervised method to pretrain a deeply joint language-knowledge foundation model from text and KG at scale. > > Specifically, our model takes pairs of text segments and relevant KG subgraphs as input and bidirectionally fuses information from both modalities.
        
        2022-10-23 About
        
        [2208.01066] What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
        
        Tags:
        
        Arxiv Doc
        NLP@Stanford
        Prompting/In-context learning
        Transformers
        
        the NN learns *how to learn* linear regression, decision trees, 2-layer ReLU nets!
        
        2022-09-17 About
        
        Stanford Open Virtual Assistant Lab
        
        Tags:
        
        NLP@Stanford
        Virtual Assistant
        
        2022-06-15 About
        
        [2204.08173] TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
        
        Tags:
        
        Arxiv Doc
        Discuté avec Raphaël
        Entity linking
        Entity Retrieval
        Entity type
        NLP@Stanford
        Open domain tasks
        
        a method for training entity retrievers on knowledge graph types and unstructured text > Entity retrieval--retrieving information about entity mentions in a query--is a key step in open-domain tasks, such as question answering or fact checking. However, state-of-the-art entity retrievers struggle to retrieve rare entities for ambiguous mentions > A promising approach to overcome popularity biases is to incorporate types (e.g., athlete or politician) from a knowledge graph into the retriever. A key advantage of types is that contextual cues learned over popular entities can generalize to rare entities of the same types. > Our key insight is that type information should also be learned contrastively, as opposed to more straightforward approaches like adding the type as textual input
        
        2022-05-11 About
        
        [2006.05987] Revisiting Few-sample BERT Fine-tuning
        
        Tags:
        
        ADAM (ML)
        Arxiv Doc
        BERT fine-tuning
        BERT fine-tuning: instabilities
        NLP@Stanford
        
        > A study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. > The most commonly used optimizer for fine-tuning BERT is BERTADAM, a modified version of the ADAM first-order stochastic optimization method. It differs from the original ADAM algorithm (Kingma & Ba, 2014) in omitting a bias correction step. > > ... We observe that the bias correction omission influences the learning rate, especially early in the fine-tuning process, and is one of the primary reasons for instability in fine-tuning BERT and this is bad when finetuning with less than 10K samples. Pb included in many > open source libraries, including the official implementation huggingface’s Transformers How to solve pb in HuggingFace? > HuggingFace Transformers AdamW has correct_bias parameter set to True by default. Still it's worth noting the importance this parameter serves. [src](doc:2022/08/on_stability_of_few_sample_tran)
        
        2022-03-21 About
        
        Document Matching for Job Descriptions | Semantic Scholar (2021)
        
        Tags:
        
        NLP@Stanford
        NLP + Human Resources
        Sentence-BERT
        
        > We train a document encoder to match online job descriptions to one of many standardized job roles from Singapore’s Skills Framework. The encoder generates semantically meaningful document encodings from textual descriptions of job roles, which are then compared using Cosine Similarity to determine matching. During training, we implement the methodology used by Sentence-BERT, fine tuning pre-trained BERT models using a siamese network architecture on labelled document pairs.
        
        2022-03-09 About
        
        Stanford NLP Group sur Twitter : "...how to use AI systems to augment the work of humans in interactive systems "
        
        Tags:
        
        Human-AI collaboration
        Human in the loop
        NLP@Stanford
        Tweet
        
        2022-03-05 About
        
        AK sur Twitter : "You Only Need One Model for Open-domain Question Answering..."
        
        Tags:
        
        Chris Manning
        NLP@Stanford
        Open Domain Question Answering
        Tweet
        
        2021-12-23 About
        
        Seth Stafford sur Twitter : "Here’s a nice paper (ICLR spotlight) on how to apply masking in LM training..."
        
        Tags:
        
        MLM
        NLP: pretraining
        NLP@Stanford
        Pointwise mutual information
        Transformers
        Tweet
        
        > You can read this paper two ways: > 1. As a practical speed-up technique for training large LMs. > 2. Theoretical validation that Transformers are powerful because they ‘learn PMI’. On sentence summary of paper: Joint masking of correlated tokens significantly speeds up and improves BERT's pretraining
        
        2021-10-16 About
        
        Selective Classification Can Magnify Disparities Across Groups | SAIL Blog
        
        Tags:
        
        NLP@Stanford
        Selective Classification
        
        > Selective classification, where models can abstain when they are unsure about a prediction, routinely improves average accuracy. Worryingly, we show that s.c. can also hurt accuracy on certain subgroups of the data. [twitter](https://twitter.com/ErikJones313/status/1448681482176790532)
        
        2021-10-16 About
        
        VaLaR NMT: Vastly Lacking Resources Neural Machine Translation (2019)
        
        Tags:
        
        Low-Resource Languages
        Neural machine translation
        NLP@Stanford
        
        > We focus on extremely low-resource setting, where we are **limited to less than 10k parallel data and no mono-lingual corpora**... we create a characterdecoder-based seq2seq NMT model as a baseline and compare its performance on various levels of data scarcity. Then, we explore the performance benefit of transfer learning by training a model on a different language. .. Lastly, we use **language models and a noisy dictionary to augment our training data**. Utilizing both transfer learning and data augmentation, we see a 1.5 BLEU score improvement over the baseline
        
        2021-10-14 About
        
        Detecting Duplicate Questions (2019)
        
        Tags:
        
        Duplicate Detection
        NLP@Stanford
        Quora Question Pairs
        
        2021-10-14 About
        
        [2104.12016] Learning Passage Impacts for Inverted Indexes
        
        Tags:
        
        Arxiv Doc
        NLP@Stanford
        Retrieval-based NLP
        
        Mentionned in [Building Scalable, Explainable, and Adaptive NLP Models with Retrieval | SAIL Blog](doc:2021/10/building_scalable_explainable_)
        
        2021-10-08 About
        
        Building Scalable, Explainable, and Adaptive NLP Models with Retrieval | SAIL Blog
        
        Tags:
        
        ColBERT
        Good
        NLP@Stanford
        Not Encoding Factual Knowledge in Language Model
        Retrieval augmented LM
        Retrieval-based NLP
        
        > The black-box nature of large language models like T5 and GPT-3 makes them inefficient to train and deploy, opaque in their knowledge representations and in backing their claims with provenance, and static in facing a constantly evolving world and diverse downstream contexts. **This post explores retrieval-based NLP, where models retrieve information pertinent to solving their tasks from a plugged-in text corpus**. > > Retrieval-based NLP methods view tasks as “open-book” exams: knowledge is encoded explicitly in the form of a text corpus like Wikipedia, the medical literature, or a software’s API documentation. When solving a language task, **the model learns to search for pertinent passages** and to then use the retrieved information for crafting knowledgeable responses. In doing so, **retrieval helps decouple the capacity that language models have for understanding text from how they store knowledge**
        
        2021-10-07 About
        
        stanfordnlp/stanza: Official Stanford NLP Python Library for Many Human Languages
        
        Tags:
        
        NLP@Stanford
        Python library
        Stanza
        
        2021-09-20 About
        
        Link Prediction with Graph Neural Networks and Knowledge Extraction
        
        Tags:
        
        AI@Stanford
        Graph neural networks
        Knowledge Extraction
        Link Prediction
        NLP@Stanford
        
        > Many GNN layers have been able to be applied to the link prediction task directly. But due to some graph structure and graph neural network limitations, the performance of the neural style link prediction sometimes will be negatively influenced. To address these issues, we propose a novel approach to implicitly guide GNN with extracted knowledge.
        
        2021-09-03 About
        
        A Moderate Proposal for Radically Better AI-powered Web Search
        
        Tags:
        
        ColBERT
        Neural Search
        NLP@Stanford
        Web search
        
        2021-07-10 About
        
        A Study of multilabel text classification and the effect of label hierarchy (2015)
        
        Tags:
        
        Hierarchical multi-label text classification
        Multi-label Text classification
        NLP@Stanford
        Survey / Review
        
        They have made an implementaion of a Multi-Label Classification algorithm on Tree- and DAG-Structured Label Hierarchies ([GitHub](https://github.com/sushobhannayak/cssag))
        
        2020-08-15 About
        
        [2006.09462] Selective Question Answering under Domain Shift
        
        Tags:
        
        Arxiv Doc
        NLP@Stanford
        Question Answering
        Softmax
        Trust in NLP
        Uncertainty in Deep Learning
        
        **How you can get a QA model to abstain from answering when it doesn’t know the answer.** > Abstention policies based solely on the model's softmax probabilities fare poorly, since **models are overconfident on out-of-domain inputs**. Instead, we train a calibrator to identify inputs on which the QA model errs, and abstain when it predicts an error is likely.
        
        2020-06-30 About
        
        Softmax classifier (CS231n Convolutional Neural Networks for Visual Recognition)
        
        Tags:
        
        Andrej Karpathy
        Cross-Entropy
        cs224n
        NLP@Stanford
        Softmax
        
        > the cross-entropy objective wants the predicted distribution to have all of its mass on the correct answer.
        
        2020-06-04 About
        
        BERT, ELMo, & GPT-2: How Contextual are Contextualized Word Representations? | SAIL Blog
        
        Tags:
        
        Bertology
        Contextualized word representations
        EMNLP 2019
        NLP@Stanford
        
        2020-03-28 About
        
        Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019
        
        Tags:
        
        BERT
        Contextualized word representations
        ELMo
        NLP@Stanford
        
        2020-01-05 About
        
        CS224U: Natural Language Understanding
        
        Tags:
        
        NLP@Stanford
        NLU
        Online Course Materials
        
        2020-01-05 About
        
        [1911.00172] Generalization through Memorization: Nearest Neighbor Language Models
        
        Tags:
        
        Arxiv Doc
        Dan Jurafsky
        Generalization
        kNN-Transformers
        Knowledge-augmented Language Models
        NLP@Facebook
        NLP@Stanford
        
        extend LMs with nearest neighbor search in embedding space > kNN-LM, an approach that extends a pre-trained LM by linearly interpolating its next word distribution with a k-nearest neighbors (kNN) model > > This approach allows rare patterns to be memorized explicitly, rather than implicitly in model parameters > The kNN-LM involves augmenting such a pre-trained LM with a nearest neighbors retrieval mechanism, without any additional training (the representations learned by the LM remain unchanged). This can be done with a single forward pass over a text collection (potentially including the original LM training set), where the resulting context-target pairs are stored in a key-value datastore that is queried during inference
        
        2019-12-20 About
        
        Answering Complex Open-domain Questions at Scale | SAIL Blog
        
        Tags:
        
        NLP@Stanford
        Open Domain Question Answering
        
        2019-10-21 About
        
        CS224n: Natural Language Processing with Deep Learning Stanford / Winter 2019
        
        Tags:
        
        cs224n
        NLP@Stanford
        
        2019-09-30 About
        
        CS224n: Natural Language Processing with Deep Learning
        
        Tags:
        
        cs224n
        NLP@Stanford
        
        2019-04-03 About
        
        Introduction to PyTorch Code Examples
        
        Tags:
        
        NLP@Stanford
        PyTorch
        
        2019-04-03 About
        
        StanfordNLP | StanfordNLP
        
        Tags:
        
        NLP@Stanford
        Python-NLP
        
        2019-01-30 About
        
        The Stanford Question Answering Dataset
        
        Tags:
        
        NLP@Stanford
        Question Answering
        
        2018-11-05 About
        
        Training Classifiers with Natural Language Explanations
        
        Tags:
        
        NLP@Stanford
        Text Classification
        
        > a framework for training classifiers in which an **annotator** provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5–100 faster by providing explanations instead of just labels
        
        2018-06-23 About
        
        Context is Everything: Finding Meaning Statistically in Semantic Spaces (CS224n 2018)
        
        Tags:
        
        NLP@Stanford
        Sentence Embeddings
        
        a new take on sentence embeddings
        
        2018-04-05 About
        
        Exploring neural architectures for NER (CS224N 2018)
        
        Tags:
        
        bi-LSTM
        Named Entity Recognition
        NLP@Stanford
        
        2018-04-05 About
        
        CS224n: Natural Language Processing with Deep Learning
        
        Tags:
        
        Deep NLP
        NLP@Stanford
        
        Course Project Reports for 2018. [Notes on reddit](https://www.reddit.com/r/MachineLearning/comments/89i9h8/ps_the_2018_stanford_cs224n_nlp_course_projects/)
        
        2018-04-05 About
        
        Effective Word Representation for Named Entity Recognition (2017)
        
        Tags:
        
        bi-LSTM
        Named Entity Recognition
        NLP@Stanford
        
        > we propose a word representation that includes both the word-level and character-level information
        
        2018-03-06 About
        
        CS224n: Natural Language Processing with Deep Learning
        
        Tags:
        
        Chris Manning
        Deep NLP
        NLP@Stanford
        Richard Socher
        
        [Notes winter17](https://github.com/stanfordnlp/cs224n-winter17-notes)
        
        2017-09-10 About
        
        Word Meaning and Similarity - Stanford University
        
        Tags:
        
        Dan Jurafsky
        NLP@Stanford
        Slides
        Text Similarity
        
        thesaurus based meaning, Distributional models of meaning Term-Context matrix. Term-document matrix: use tf-idf instead of raw term counts, for the term-context matrix, use Positive Pointwise Mutual Information (PPMI: Do words x and y co-occur more than if they were independent?)
        
        2017-07-20 About
        
        Stanford Topic Modeling Toolbox
        
        Tags:
        
        NLP@Stanford
        Topic Modeling
        
        2017-05-23 About
        
        GloVe: Global Vectors for Word Representation
        
        Tags:
        
        Chris Manning
        GloVe
        NLP@Stanford
        Richard Socher
        
        2017-05-18 About
        
        Introduction to Information Retrieval, Cambridge University Press (2008) Manning, Raghavan, and Schütze
        
        Tags:
        
        AI: books & journals
        Chris Manning
        Coursera: NLP class
        Good
        Information retrieval
        Information retrieval: techniques
        NLP@Stanford
        
        2012-04-01 About
    - Coursera: NLP class
    - NLP@Stanford
    - Protégé
    - TAP
  - University of Maryland
- USA: Foreign policy
- USA : histoire
- USA: South
- US vs Europe

0 Document (Long List)

Properties

rdf:type : sl:Tag
skos:prefLabel : Amérique@fr