Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Extracting and composing robust features with denoising autoencoders
20084.4k citationsPascal Vincent, Hugo Larochelle et al.profile →
Countries citing papers authored by Pascal Vincent
Since
Specialization
Citations
This map shows the geographic impact of Pascal Vincent's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pascal Vincent with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pascal Vincent more than expected).
This network shows the impact of papers produced by Pascal Vincent. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pascal Vincent. The network helps show where Pascal Vincent may publish in the future.
Co-authorship network of co-authors of Pascal Vincent
This figure shows the co-authorship network connecting the top 25 collaborators of Pascal Vincent.
A scholar is included among the top collaborators of Pascal Vincent based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Pascal Vincent. Pascal Vincent is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Gidel, Gauthier, et al.. (2020). A Closer Look at the Optimization Landscapes of Generative Adversarial Networks. International Conference on Learning Representations.4 indexed citations
2.
Bouthillier, Xavier, César Laurent, & Pascal Vincent. (2019). Unreproducible Research is Reproducible. International Conference on Machine Learning. 725–734.20 indexed citations
3.
Touati, Abdelaziz, Pierre‐Luc Bacon, Doina Precup, & Pascal Vincent. (2018). Convergent Tree Backup and Retrace with Function Approximation. International Conference on Machine Learning. 4955–4964.4 indexed citations
4.
Gidel, Gauthier, et al.. (2018). A Variational Inequality Perspective on Generative Adversarial Networks. arXiv (Cornell University).8 indexed citations
5.
George, Thomas, César Laurent, Xavier Bouthillier, Nicolas Ballas, & Pascal Vincent. (2018). Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis. Neural Information Processing Systems. 31. 9550–9560.9 indexed citations
6.
Brébisson, Alexandre de & Pascal Vincent. (2016). An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family. International Conference on Learning Representations.11 indexed citations
7.
Vincent, Pascal. (2015). Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets.. International Conference on Learning Representations. 28. 1108–1116.8 indexed citations
8.
Rifai, Salah, Yann Dauphin, Pascal Vincent, & Yoshua Bengio. (2012). A Generative Process for Contractive Auto-Encoders.. International Conference on Machine Learning.3 indexed citations
Mesnil, Grégoire, Yann Dauphin, Xavier Glorot, et al.. (2011). Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. International Conference on Machine Learning. 97–110.90 indexed citations
12.
Desjardins, Guillaume, Aaron Courville, Yoshua Bengio, Pascal Vincent, & Olivier Delalleau. (2010). Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines. International Conference on Artificial Intelligence and Statistics. 145–152.54 indexed citations
Larochelle, Hugo, Dumitru Erhan, & Pascal Vincent. (2009). Deep Learning using Robust Interdependent Codes. International Conference on Artificial Intelligence and Statistics. 312–319.17 indexed citations
15.
Erhan, Dumitru, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, & Pascal Vincent. (2009). The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training. International Conference on Artificial Intelligence and Statistics. 153–160.226 indexed citations
16.
Bengio, Yoshua, Hugo Larochelle, & Pascal Vincent. (2005). Non-Local Manifold Parzen Windows. Neural Information Processing Systems. 18. 115–122.31 indexed citations
17.
Bengio, Yoshua, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, & Patrice Marcotte. (2005). Convex Neural Networks. Neural Information Processing Systems. 18. 123–130.58 indexed citations
Chapados, Nicolas, Yoshua Bengio, Pascal Vincent, et al.. (2001). Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference. Neural Information Processing Systems. 14. 1369–1376.12 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.