Hugo Larochelle
I am a Principal Scientist in the Google DeepMind team in Montreal. My main area of expertise is deep learning. My previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models and zero-shot learning. More broadly, I’m interested in applications of deep learning to natural language processing, code, computer vision and environmental sustainability problems.
Previously, I was Associate Professor at the Université de Sherbrooke (UdeS). I also co-founded Whetlab, which was acquired in 2015 by Twitter, where I then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, I was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. I obtained my Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio.
My academic involvement includes being a member of the boards for the International Conference on Machine Learning (ICML) and for the Neural Information Processing Systems (NeurIPS) conference. I also co-founded the journal Transactions on Machine Learning Research.
Finally, I have a popular online course on deep learning and neural networks, freely accessible on YouTube.
Previously, I was Associate Professor at the Université de Sherbrooke (UdeS). I also co-founded Whetlab, which was acquired in 2015 by Twitter, where I then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, I was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. I obtained my Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio.
My academic involvement includes being a member of the boards for the International Conference on Machine Learning (ICML) and for the Neural Information Processing Systems (NeurIPS) conference. I also co-founded the journal Transactions on Machine Learning Research.
Finally, I have a popular online course on deep learning and neural networks, freely accessible on YouTube.
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Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
Rishab Goel
International Conference on Learning Representations (ICLR) (2023)
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
Mike Mozer
Proceedings of the 39th International Conference on Machine Learning, PMLR (2022)
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Neil Houlsby
Xiaohua Zhai
Sylvain Gelly
NeurIPS Datasets and Benchmarks Track (2021)
Impact of Aliasing on Generalization in Deep Convolutional Networks
Nicolas Le Roux
Rob Romijnders
International Conference on Computer Vision ICCV 2021, IEEE/CVF (2021)
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
Thirty-fourth Conference on Neural Information Processing Systems (2020)
Revisiting Fundamentals of Experience Replay
Liam B. Fedus
Mark Rowland
Prajit Ramachandran
Will Dabney
Yoshua Bengio
International Conference on Machine Learning (2020)
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou
Tyler Zhu
Kelvin Xu
Carles Gelada
International Conference on Learning Representations (submission) (2020)
