Semanlink - Microsoft Research AND AI@Microsoft

GraphRAG: Unlocking LLM discovery on narrative private data - Microsoft Research

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2024-03-07 A propos

New Theory Suggests Chatbots Can Understand Text | Quanta Magazine

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2024-02-11 A propos

Sebastien Bubeck sur X : "How far does one billion parameters take you? ... Releasing phi-1.5, a 1.3B parameter LLM exhibiting emergent behaviors surprisingly close to much larger LLMs"

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2023-09-12 A propos

Physics of AI - YouTube

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- Intelligence has emerged: why? how?
- Let's study this with *controlled experiments* and *toy models*
- Clean and clear insights that peer slightly behind the magic curtain

emergence; rise of scale. Phase transition: fct(compute time for training: nb of params, dataset size)

(12:25) Transformers: the jump when compared to traditional NN: instead on oparating on single input X, operates on a set of inputs. (cf. training to say whether an image contains 2 elements of the same kind). **The aptitude to compare elements in the input sequence enables analogies** - the essence of reasoning.(Classical NN treats one high dimensional vector, tranformers hanlde sets of vectors. Attention layer replaces learned filters W of classical NN by the other elements in the sequence. Attention module compares part of input against other part of input. "Relative machines rather than absolute". Sets are a very powerful level of abstraction)

emergence
> The truth is that nobody has a clue what is going on"
>
> "**Something unknown is doing we don't know what**" (A. Eddington)

being inspired by physics' methodology: controled experiments and toy mathematical models

2023-03-28 A propos

BioGPT

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2023-02-02 A propos

Revolutionizing Document AI with Multimodal Document Foundation Models - Microsoft Research

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2023-01-30 A propos

Unveiling Transformers with LEGO - YouTube

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2022-06-30 A propos

Lenka Zdeborova sur Twitter : "Amazing lecture of @SebastienBubeck giving intuition on transformers and how to approach them theoretically..."

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2022-06-30 A propos

Everything you always wanted to know about extreme classification (but were afraid to ask) - Microsoft Research - 2019

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2020-08-12 A propos

[2001.09522] TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

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how to add a set of new concepts to an existing taxonomy.

[Tweet](https://twitter.com/mickeyjs6/status/1253772146142216194?s=20) [GitHub](https://github.com/mickeystroller/TaxoExpan)

> we study the taxonomy expansion task: given an
existing taxonomy and a set of new emerging concepts, we aim
to automatically expand the taxonomy to incorporate these new
concepts (without changing the existing relations in the given taxonomy).

> To the best of our knowledge, this is the first study on **how to
expand an existing directed acyclic graph (as we model a taxonomy
as a DAG) using self-supervised learning**.

Self-supervised framework, the existing taxonomy being used as training data: it learns a model to predict whether a query concept is the direct hyponym of an anchor concept.

> 2 techniques:
>
> 1. a **position-enhanced graph neural network that encodes the local structure of an anchor concept** in the existing taxonomy,
> 2. a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.

Regarding 1: uses [GNN](/tag/graph_neural_networks.html) to model the "ego network" of concepts (potential “siblings”
and “grand parents” of the query concept).

> Regular
GNNs fail to distinguish nodes with different relative positions to
the query (i.e., some nodes are grand parents of the query while
the others are siblings of the query). To address this limitation, we
present a simple but effective enhancement to inject such position
information into GNNs using position embedding. We show that
such embedding can be easily integrated with existing GNN architectures
(e.g., [GCN](/tag/graph_convolutional_networks) and GAT) and significantly boosts the
prediction performance

Regarding point 2: uses InfoNCE loss, cf. [Contrastive Predictive Coding](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1807.03748)

> Instead of predicting
whether each individual ⟨query concept, anchor concept⟩ pair
is positive or not, we first group all pairs sharing the same query
concept into a single training instance and learn a model to select
the positive pair among other negative ones from the group.

(Hum, ça me rappelle quelque chose)

> assume each concept (in existing taxonomy + set of new concepts) has an initial embedding
vector learned from some text associated with this concept.

To keep things tractable, only attempts to find a single parent node of each new concept.

2020-04-25 A propos

Neural Models for Information Retrieval (2017)

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2019-08-18 A propos

Bringing the power of machine reading comprehension to specialized documents - Microsoft Research

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2019-07-28 A propos

[1602.01137] A Dual Embedding Space Model for Document Ranking

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2019-07-17 A propos

Microsoft Concept Graph and Concept Tagging Release

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Microsoft Concept Graph

2019-05-28 A propos

Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding | MIT Press Journals (2019)

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2019-05-28 A propos

Bringing low-resource languages and spoken dialects into play with Semi-Supervised Universal Neural Machine Translation - Microsoft Research

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2018-05-18 A propos

Knowledge Graph and Text Jointly Embedding (2014)

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2018-01-05 A propos

Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems - Microsoft Research (2015)

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> new simple, yet effective approaches to
learn domain specific word embeddings.

## Intro

> Adapting word embeddings, such as jointly capturing
syntactic and semantic information, can further enrich semantic
word representations for several tasks, e.g., sentiment
analysis (Tang et al. 2014), named entity recognition
(Lebret, Legrand, and Collobert 2013), entity-relation extraction
(Weston et al. 2013), etc. (Yu and Dredze 2014)
has introduced a lightly supervised word embedding learning
extending word2vec. They incorporate prior information to the objective
function as a regularization term considering synonymy relations
between words from Wordnet (Fellbaum 1999).

> In this work, we go one step further and investigate if
enriching the word2vec word embeddings trained on unstructured/
unlabeled text with domain specific semantic relations
obtained from knowledge sources (e.g., knowledge
graphs, search query logs, etc.) can help to discover relation
aware word embeddings. Unlike earlier work, **we encode the
information about the relations between phrases, thereby,
entities and relation mentions are all embedded into a low dimensional
vector space**.

## Related work (Learning Word Embeddings with Priors)

- word2vec
- Relational Constrained Model (RTM) (Yu and Dredze 2014)
While CBOW learns lexical word embeddings from provided text, the RTM learns embeddings of words based on their similarity to other words provided by a knowledge resource (eg. wordnet)
- Joint model  (Yu and Dredze 2014)
combines CBOW and RTM through linear combination

2017-10-02 A propos

Vectorland: Brief Notes from Using Text Embeddings for Search

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2017-09-18 A propos

Using Text Embeddings for Information Retrieval

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2017-09-18 A propos

A Ranking Approach to Keyphrase Extraction - Microsoft Research (2009)

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2017-06-27 A propos

Challenges of the email domain for text classification

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2015-10-20 A propos