Hugo Larochelle
Appointment
Advisory Committee Member
Learning in Machines & Brains
About
Hugo Larochelle is an associate fellow in CIFAR’s Learning in Machines & Brains program and a Principal Scientist in the Google DeepMind team in Montreal. His main area of expertise is deep learning. His previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models and zero-shot learning. More broadly, he is interested in applications of deep learning to natural language processing, code, computer vision and environmental sustainability problems.
He co-founded Whetlab, which was acquired in 2015 by Twitter, where he then worked as a Research Scientist in the Twitter Cortex group. Hugo’s 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. He also co-founded the journal Transactions on Machine Learning Research.
Hugo has a popular online course on deep learning and neural networks, freely accessible on YouTube.
Awards
- Google Faculty Research Award, 2013 and 2012
- NSERC Discovery Grant, 2012
- AISTATS Notable Paper Award, 2011
Relevant Publications
- Teng, M., Elmustafa, A., Akera, B., Bengio, Y., Radi, H., Larochelle, H., Rolnick, D. (2023). Satbird: a dataset for bird species distribution modeling using remote sensing and citizen science data. In Proceedings of the Neural Information Processing System conference - Datasets and Benchmarks Track.
- Shrivastava, D., Larochelle, H., & Tarlow, D. (2023). Repository-level prompt generation for large language models of code. In International Conference on Machine Learning (pp. 31693 - 31715). PMLR.
- Afrasiyabi, A., Larochelle, H., Lalonde, J-F., & Gagné, C. (2022). Matching feature sets for few-shot image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp.9014-9024).
- Zhou, H., Vani, A., Larochelle, H., Courville, A. (2022). Fortuitous forgetting in connectionist networks. In Proceedings of the International Conference on Learning Representations.
Larochelle, H., & Murray, I. (2011). The neural autoregressive distribution estimator. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (pp. 29-37).
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P. A., & Bottou, L. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(12).
Bengio, Y. et al. (2007). Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst 19.