Portrait of Hugo Larochelle

Hugo Larochelle

Core Industry Member
Canada CIFAR AI Chair
Adjunct professor, Université de Montréal, Depatment of Computer Science and Operations Research
Research Scientist, Google DeepMind

Biography

I am a researcher in the Google DeepMind (previously Google Brain) team in Montréal, an adjunct professor at Université de Montréal, and a Canada CIFAR AI Chair. My research focuses on the study and development of deep learning algorithms.

Current Students

PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Principal supervisor :

Publications

Teaching Algorithmic Reasoning via In-context Learning
Hattie Zhou
Azade Nova
Behnam Neyshabur
Hanie Sedghi
Uniform Priors for Data-Efficient Learning
Samarth Sinha
Karsten Roth
Anirudh Goyal
Marzyeh Ghassemi
Zeynep Akata
Animesh Garg
Few or zero-shot adaptation to novel tasks is important for the scalability and deployment of machine learning models. It is therefore cruci… (see more)al to find properties that encourage more transferable features in deep networks for generalization. In this paper, we show that models that learn uniformly distributed features from the training data, are able to perform better transfer learning at test-time. Motivated by this, we evaluate our method: uniformity regularization (UR) on its ability to facilitate adaptation to unseen tasks and data on six distinct domains: Few-Learning with Images, Few-shot Learning with Language, Deep Metric Learning, 0-Shot Domain Adaptation, Out-of-Distribution classification, and Neural Radiance Fields. Across all experiments, we show that using UR, we are able to learn robust vision systems which consistently offer benefits over baselines trained without uniformity regularization and are able to achieve state-of-the-art performance in Deep Metric Learning, Few-shot learning with images and language.
Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi
Jean‐François Lalonde
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (see more)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.
Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi
Jean‐François Lalonde
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (see more)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.