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 Hugo Larochelle
Since
Specialization
Citations
This map shows the geographic impact of Hugo Larochelle'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 Hugo Larochelle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hugo Larochelle more than expected).
This network shows the impact of papers produced by Hugo Larochelle. 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 Hugo Larochelle. The network helps show where Hugo Larochelle may publish in the future.
Co-authorship network of co-authors of Hugo Larochelle
This figure shows the co-authorship network connecting the top 25 collaborators of Hugo Larochelle.
A scholar is included among the top collaborators of Hugo Larochelle 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 Hugo Larochelle. Hugo Larochelle is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dumoulin, Vincent, Neil Houlsby, Utku Evci, et al.. (2021). A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. Neural Information Processing Systems.2 indexed citations
3.
Zhang, Han, et al.. (2020). Small-GAN: Speeding up GAN Training using Core-Sets. International Conference on Machine Learning. 1. 9005–9015.2 indexed citations
4.
Che, Tong, Ruixiang Zhang, Jascha Sohl‐Dickstein, et al.. (2020). Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling. arXiv (Cornell University). 33. 12275–12287.3 indexed citations
5.
Caccia, M., Lucas Caccia, William Fedus, et al.. (2020). Language GANs Falling Short. International Conference on Learning Representations.34 indexed citations
6.
Goyal, Anirudh, Riashat Islam, Zafarali Ahmed, et al.. (2019). InfoBot: Transfer and Exploration via the Information Bottleneck. arXiv (Cornell University).8 indexed citations
7.
Ravi, Sachin & Hugo Larochelle. (2018). Meta-Learning for Batch Mode Active Learning.. International Conference on Learning Representations.12 indexed citations
Bazzani, Loris, Hugo Larochelle, & Lorenzo Torresani. (2016). Recurrent Mixture Density Network for Spatiotemporal Visual Attention. arXiv (Cornell University).15 indexed citations
11.
Hutter, Frank, Balázs Kégl, Rich Caruana, et al.. (2015). Automatic Machine Learning (AutoML). SPIRE - Sciences Po Institutional REpository.4 indexed citations
12.
Larochelle, Hugo, et al.. (2015). Using a recursive neural network to learn an agent's decision model for plan recognition. International Conference on Artificial Intelligence. 918–924.16 indexed citations
13.
Lacoste, Alexandre, et al.. (2014). Agnostic Bayesian Learning of Ensembles. International Conference on Machine Learning. 611–619.14 indexed citations
Bazzani, Loris, Hugo Larochelle, Vittorio Murino, Jo-Anne Ting, & Nando de Freitas. (2011). Learning attentional policies for tracking and recognition in video with deep networks. Oxford University Research Archive (ORA) (University of Oxford). 937–944.26 indexed citations
Salakhutdinov, Ruslan & Hugo Larochelle. (2010). Efficient Learning of Deep Boltzmann Machines. International Conference on Artificial Intelligence and Statistics. 693–700.200 indexed citations
18.
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
19.
Larochelle, Hugo, et al.. (2006). Distributed Representation Prediction for Generalization to New Words.1 indexed citations
20.
Bengio, Yoshua, Hugo Larochelle, & Pascal Vincent. (2005). Non-Local Manifold Parzen Windows. Neural Information Processing Systems. 18. 115–122.31 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.