--- title: "Pascal Vincent — author profile and papers" description: "Pascal Vincent: 2 papers on Elamur Research (2003–2012), h-index 46 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/pascal-vincent/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/pascal-vincent/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Pascal Vincent # Pascal Vincent Pascal Vincent works in machine learning, with papers here centred on how data can be encoded as learned representations rather than hand-designed features. A recurring question is how models can generalise from limited observations when the space of possible inputs is vast. The answer explored in this work is distributed representations: continuous vectors that place similar items close together. The work spans [language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and the broader study of deep and unsupervised feature learning. The most cited paper here is [A Neural Probabilistic Language Model](https://md.research.elamur.ai/free/en/papers/bengio-2003-neural-probabilistic-language-model/) (2003). It tackles the problem that most word sequences seen at test time never appear in training. Instead of counting short n-gram fragments, it trains a neural network that jointly learns a feature vector for every word and a next-word probability function built on those vectors. Words with related meanings or grammatical roles thus end up with nearby vectors. The second most cited, [Representation Learning: A Review and New Perspectives](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/) (2012), is a survey rather than a new model. It argues that good representations should disentangle the hidden factors behind observed data, and it organises the field around general priors such as smoothness, hierarchical concepts and semi-supervised structure. The 2012 review is the latest of the papers here. It moves from a single modelling problem to a wider view of unsupervised feature learning and deep learning as a whole. Vincent's bylines are mostly from the [Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/), with further affiliations to the [Mila - Quebec Artificial Intelligence Institute](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/), the [Canadian Institute for Advanced Research](https://md.research.elamur.ai/free/en/labs/canadian-institute-for-advanced-research/) and [Meta](https://md.research.elamur.ai/free/en/companies/meta/) (United States). Co-authors here include [Yoshua Bengio](https://md.research.elamur.ai/free/en/authors/yoshua-bengio/) and [Aaron Courville](https://md.research.elamur.ai/free/en/authors/aaron-courville/). Pascal Vincent has 2 papers in our library, in 2003–2012, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Yoshua Bengio](https://md.research.elamur.ai/free/en/authors/yoshua-bengio/), [Aaron Courville](https://md.research.elamur.ai/free/en/authors/aaron-courville/). 2 papers in our library · 2003–2012 [OpenAlex](https://openalex.org/A5103958886) · [**h-index 46**](https://openalex.org/authors/A5103958886) · [110 works](https://openalex.org/works?filter=author.id:A5103958886) · [26,047 citations](https://openalex.org/works?filter=author.id:A5103958886&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [2 works in our library](https://md.research.elamur.ai/free/en/authors/pascal-vincent/#papers) · [1,133 citations in our library](https://md.research.elamur.ai/free/en/authors/pascal-vincent/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Star rank #1732](https://md.research.elamur.ai/free/en/paper-stars/page/18/#star-1732) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Pascal+Vincent) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Pascal+Vincent) · [dblp](https://dblp.org/search?q=Pascal+Vincent) · [ACL Anthology](https://aclanthology.org/search/?q=Pascal+Vincent) ## Affiliations [🎓**Université de Montréal** Montreal · Canada 2000–2024 · 74 papers](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/) · [🔬**Canadian Institute for Advanced Research** Toronto · Canada 2015–2024 · 8 papers](https://md.research.elamur.ai/free/en/labs/canadian-institute-for-advanced-research/) · [🏢**Meta** Menlo Park · United States 2018–2023 · 8 papers](https://md.research.elamur.ai/free/en/companies/meta/) · [🔬**Mila - Quebec Artificial Intelligence Institute** Montreal · Canada 2015–2024 · 6 papers](https://md.research.elamur.ai/free/en/labs/mila-quebec-artificial-intelligence-institute/) · [🔬**Centre de recherches mathématiques** Montreal · Canada 2000–2006 · 3 papers](https://ror.org/03czdsq06) · [🏢**Meta** Tel Aviv · Israel 2020 · 2 papers](https://md.research.elamur.ai/free/en/companies/meta-israel/) · [🏢**AT&T** Bedminster · United States 2000–2003 · 2 papers](https://md.research.elamur.ai/free/en/companies/at-t/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed. ## Topics [Language models & pretraining (1)](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2010s [**2012** 1 paper](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/) ### 2000s [**2003** 1 paper](https://md.research.elamur.ai/free/en/papers/bengio-2003-neural-probabilistic-language-model/) ## Papers The most cited first (OpenAlex and our library). 1. **[A Neural Probabilistic Language Model](https://md.research.elamur.ai/free/en/papers/bengio-2003-neural-probabilistic-language-model/)** *(2003 · 901 citations)* The paper addresses statistical language modeling, that is, estimating the probability of the next word given the preceding ones. 2. **[Representation Learning: A Review and New Perspectives](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/)** *(2012 · 316 citations)* This survey asks why the encoding of input data matters so much for machine learning, and how such encodings can be learned automatically instead of being hand-built through feature engineering. ## Also in our library 1 more work of theirs is in our library without a page here yet; it will appear as we publish them. ## Co-authors here 2 co-authors with a page, the most papers together first. - [Yoshua Bengio](https://md.research.elamur.ai/free/en/authors/yoshua-bengio/) ×2 - [Aaron Courville](https://md.research.elamur.ai/free/en/authors/aaron-courville/) ## Cited by Papers of our library cite their works 1,133 times (of the 140,884 papers whose bibliographies we read); [118 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/pascal-vincent/cited-by/).