Semanlink - Uncertainty Reasoning

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Artificial Intelligence

Uncertainty Reasoning

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52 Documents (Short List)

Christopher Manning sur X : "Now that everyone is writing LLM programs, the idea of doing approximate bayesian inference by sampling along linguistic pipelines (rather than k-best, etc.) is more relevant again"

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2024-03-19 About

Uncertainty Estimation for NLP - Conformal Prediction

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2022-10-18 About

Tutorial on Uncertainty Estimation for NLP

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2022-10-18 About

[2011.06225] A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

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2022-09-08 About

Uncertainty Estimation for Natural Language Processing – Google Research

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2022-09-07 About

[2008.07267] A Survey of Active Learning for Text Classification using Deep Neural Networks

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2022-09-06 About

Why generative modeling

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2021-01-12 About

raphaelsty/abayes: Autoregressive Bayesian linear model

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2020-09-11 About

[2006.09462] Selective Question Answering under Domain Shift

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2020-06-30 About

Max Little sur Twitter : "Causal bootstrapping - a simple way of doing causal inference using arbitrary machine learning algo..."

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2020-03-08 About

[2002.11402] Detecting Potential Topics In News Using BERT, CRF and Wikipedia

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2020-02-27 About

A Joint Model for Entity Analysis: Coreference, Typing, and Linking (Greg Durrett, Dan Klein 2014)

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2020-01-09 About

Bayesian deep learning with Fastai : how not to be uncertain about your uncertainty !

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2019-11-29 About

[1011.4088] An Introduction to Conditional Random Fields

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2019-10-13 About

[1904.13001] Encoding Categorical Variables with Conjugate Bayesian Models for WeWork Lead Scoring Engine

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2019-07-04 About

[1506.02142] Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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2019-05-13 About

Uncertainty estimation for Neural Network — Dropout as Bayesian Approximation

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2019-05-13 About

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 About

Sean J. Taylor sur Twitter : "A couple days ago another team asked me to speak about Bayesian data analysis...

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Bayesian analysis

2018-12-15 About

Durk Kingma sur Twitter : about likelihood-based generative models

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2018-12-07 About

Making Your Neural Network Say “I Don’t Know” — Bayesian NNs using Pyro and PyTorch

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2018-12-04 About

Deep Bayesian Neural Networks. – Stefano Cosentino – Medium

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Bayesian Deep Learning

2018-11-05 About

Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning for Safe AI - Home

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Bayesian Deep Learning

2018-11-05 About

Effect of Non-linear Deep Architecture in Sequence Labeling

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2018-10-22 About

Named Entity Recognition and Classification with Scikit-Learn

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2018-09-16 About

A Framework for Semi supervised Concept Extraction from MOOC content (2017)

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2018-08-12 About

Named Entity Recognition and the Road to Deep Learning (2017)

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> the old
and the new-style NLP are not diametrically
opposed: just as it is possible (and useful!) to
incorporate neural-network features into a CRF,
CRFs have influenced some of the best deep
learning models for sequence labelling

This blog post go through the ways of doing NER, starting with CRF:

- When you develop a CRF,
a lot of time goes into finding feature functions (Does a word start with a capital? Is it uppercase? Is it a digit?...) 
- Pb: semantic similarity between words. 
- Gazetteers: lists with names of people, locations and organizations that are known in advance.
- feed Word Embeddings to a CRF: one way is to cluster a set
of word embeddings by distributional similarity, and
provide the CRF with the cluster IDs of a token and its
context words.
- Use word and character based embeddings
- LSTM not good enough -> biLSTM
- **biLSTM predict all labels independently of each other -> add a CRF layer** which outputs a matrix of transition scores between two states: dynamic programming can help find the optimal tag sequence for the sentence

2018-05-20 About

Named Entity Recognition and the Stanford NER Software (slides)

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2018-05-20 About

Stanford Named Entity Recognizer

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Stanford NER

2018-05-20 About

How to Train your Own Model with NLTK and Stanford NER Tagger? (for English, French, German…)

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2018-05-20 About

[1508.01991] Bidirectional LSTM-CRF Models for Sequence Tagging

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2018-03-05 About

Sequence Tagging with Tensorflow

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2018-03-05 About

Named Entity Recognition using Word Embedding as a Feature (2016)

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2017-10-01 About

On Bayesian inference, maximum entropy and Support Vector Machines methods

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2014-04-25 About

L'implémentation neuronale des mécanismes Bayésiens - Psychologie cognitive expérimentale - Stanislas Dehaene - Collège de France

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2013-12-22 About

mathematicalmonk's channel - YouTube

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2013-05-24 About

Bayes networks: How to use D-separation - illustrative examples - YouTube

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2013-05-24 About

Probabilistic Graphical Models (book, Koller)

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2013-05-23 About

Probabilistic Reasoning in the Semantic Web using Markov Logic

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2010-07-19 About

Facts versus Factions: the use and abuse of subjectivity in scientific research - PART 2

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2009-01-06 About

Semantic Web and uncertainty « Ivan’s private site

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2008-10-16 About

Uncertainty Reasoning for the World Wide Web

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2008-05-04 About

W3C Semantic Web Activity News - Report of the “Uncertainty Reasoning for the World Wide Web” Incubator Group at W3C

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2008-04-17 About

W3C Uncertainty Reasoning for the World Wide Web Incubator Group

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2007-03-16 About

Modeling Uncertainty in Semantic Web Taxonomies

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2006-12-06 About

PR-OWL: A Bayesian Framework for the Semantic Web

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2006-11-07 About

Ivan's Blog : Workshop on Uncertainty Reasoning on the SW

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Uncertainty Reasoning AND Semantic Web

2006-11-07 About

PR-OWL: A Bayesian Ontology Language for the Semantic Web

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2006-09-07 About

UMBC eBiquity - Publications - Probabilistic Framework for Semantic Web - OWL and Bayesian Networks

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2006-05-01 About

Hugin

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Bayesian analysis

2006-02-17 About

UMBC eBiquity Project: Bayes OWL

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2005-07-04 About

UMBC eBiquity Publication: A Bayesian Methodology towards Automatic Ontology Mapping

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2005-06-15 About

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sl:creationDate : 2006-11-07
rdf:type : sl:Tag
skos:prefLabel : Uncertainty Reasoning