Semanlink - Université AND Technical girls and guys

> an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon **insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems** that leverage and extend the state of the art in machine learning **by integrating human and machine intelligence**. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants.

> [#Dehaene](/tag/stanislas_dehaene)'s work extends the [#Global Workspace Theory](/tag/global_workspace_theory) of Bernard Baars. Dehaene’s version of the theory combined with Yoshua Bengio’s concept of a [#consciousness prior](/tag/consciousness_prior.html) and deep reinforcement learning suggest a model for constructing and maintaining the cognitive states that arise and persist during complex problem solving.

2019-11-12 About

[1703.02507] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Tags:

2019-03-25 About

[1902.09229] A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Tags:

2019-03-20 About

Contrastive Unsupervised Learning of Semantic Representations: A Theoretical Framework – Off the convex path (2019-03)

Tags:

2019-03-20 About

Word Embeddings: Explaining their properties – Off the convex path (2016)

Tags:

2019-03-20 About

nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

Tags:

2019-02-07 About

Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline (Ethayarajh 2018)

Tags:

2018-10-08 About

Linear algebraic structure of word meanings – Off the convex path

Tags:

2018-09-20 About

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:

2018-09-18 About

Off the convex path

Tags:

2018-09-09 About

A Latent Variable Model Approach to PMI-based Word Embeddings (2016)

Tags:

2018-08-28 About

[1601.03764] Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Tags:

> 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: