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D-Index & Metrics

Computer Science

D-Index
68
Citations
92771
World Ranking
2014
National Ranking
1016

Overview

Hugo Larochelle is a researcher affiliated with Google in the United States, specializing in the field of Computer Science with a focus on Artificial Intelligence and its subfields. Their work primarily addresses topics such as Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, Adversarial Robustness in Machine Learning, Software Engineering Research, Topic Modeling, Machine Learning and Data Classification, and Advanced Neural Network Applications.

Larochelle has contributed extensively to academic publishing, with a notable presence in venues such as arXiv (Cornell University), where they have published 37 papers. Other frequent publication venues include the Proceedings of the AAAI Conference on Artificial Intelligence, the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Medical Image Analysis, and Zenodo (CERN European Organization for Nuclear Research).

The researcher often collaborates with a consistent group of co-authors. Frequent collaborators include Vincent Dumoulin, Daniel Tarlow, Yoshua Bengio, Samarth Sinha, and Anirudh Goyal, reflecting a collaborative approach across multiple projects in machine learning and artificial intelligence research.

Among Hugo Larochelle's recent papers are:

  • Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program), 2020, arXiv (Cornell University)
  • Matching Feature Sets for Few-Shot Image Classification, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Revisiting Fundamentals of Experience Replay, 2020, arXiv (Cornell University)
  • A Universal Representation Transformer Layer for Few-Shot Image Classification, 2020, arXiv (Cornell University)
  • Interpretable Multi-Modal Hate Speech Detection, 2021, arXiv (Cornell University)

Best Publications

  • Advances in Neural Information Processing Systems 31

    S. Bengio;H.M. Wallach;H. Larochelle;K. Grauman

  • Extracting and composing robust features with denoising autoencoders

    Pascal Vincent;Hugo Larochelle;Yoshua Bengio;Pierre-Antoine Manzagol

  • Practical Bayesian Optimization of Machine Learning Algorithms

    Jasper Snoek;Hugo Larochelle;Ryan P Adams

  • Domain-adversarial training of neural networks

    Yaroslav Ganin;Evgeniya Ustinova;Hana Ajakan;Pascal Germain

  • Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

    Pascal Vincent;Hugo Larochelle;Isabelle Lajoie;Yoshua Bengio

  • Greedy Layer-Wise Training of Deep Networks

    Yoshua Bengio;Pascal Lamblin;Dan Popovici;Hugo Larochelle

  • Brain tumor segmentation with Deep Neural Networks

    Mohammad Havaei;Axel Davy;David Warde-Farley;Antoine Biard

  • Optimization as a Model for Few-Shot Learning

    Sachin Ravi;Hugo Larochelle

  • Autoencoding beyond pixels using a learned similarity metric

    Anders Boesen Lindbo Larsen;Søren Kaae Sønderby;Hugo Larochelle;Ole Winther

  • Exploring Strategies for Training Deep Neural Networks

    Hugo Larochelle;Yoshua Bengio;Jérôme Louradour;Pascal Lamblin

  • An empirical evaluation of deep architectures on problems with many factors of variation

    Hugo Larochelle;Dumitru Erhan;Aaron Courville;James Bergstra

  • Proceedings of The 32nd International Conference on Machine Learning

    Mathieu Germain;Karol Gregor;Iain Murray;Hugo Larochelle

  • Describing Videos by Exploiting Temporal Structure

    Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas

  • Classification using discriminative restricted Boltzmann machines

    Hugo Larochelle;Yoshua Bengio

  • Meta-Learning for Semi-Supervised Few-Shot Classification

    Eleni Triantafillou;Hugo Larochelle;Jake Snell;Josh Tenenbaum

  • MADE: Masked Autoencoder for Distribution Estimation

    Mathieu Germain;Karol Gregor;Iain Murray;Hugo Larochelle

  • Meta-Learning for Semi-Supervised Few-Shot Classification

    Mengye Ren;Eleni Triantafillou;Sachin Ravi;Jake Snell

  • The Neural Autoregressive Distribution Estimator

    Hugo Larochelle;Iain Murray

  • ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Oskar Maier;Bjoern H. Menze;Janina von der Gablentz;Levin Häni

  • Learning to combine foveal glimpses with a third-order Boltzmann machine

    Hugo Larochelle;Geoffrey E. Hinton

  • Zero-data learning of new tasks

    Hugo Larochelle;Dumitru Erhan;Yoshua Bengio

  • Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

    Eleni Triantafillou;Tyler Zhu;Vincent Dumoulin;Pascal Lamblin

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Aaron Courville
Aaron Courville University of Montreal
Ryan P. Adams
Ryan P. Adams Princeton University
Iain Murray
Iain Murray University of Edinburgh
Chris Pal
Chris Pal Polytechnique Montréal
Richard S. Zemel
Richard S. Zemel University of Toronto
Pierre-Marc Jodoin
Pierre-Marc Jodoin Université de Sherbrooke
François Laviolette
François Laviolette Université Laval
Pascal Vincent
Pascal Vincent Facebook (United States)
Kevin Swersky
Kevin Swersky Google (United States)

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