I am interested in building machine learning algorithms that automatically adapt to novel circumstances in the presence of limited data. My recent work focuses on few-shot learning and Bayesian deep learning.
- 2/12/2021: I submitted the final version of my PhD thesis.
- 1/12/2021: Our paper on Gaussian processes for few-shot classification was accepted to ICLR 2021.
- 1/11/2021: I successfully defended my PhD thesis. Many thanks to Amos Storkey for serving as my external examiner.
- Flexible Few-Shot Learning with Contextual Similarity.
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, and Richard Zemel.
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. Jake Snell and Richard Zemel. ICLR 2021.
arxiv | code
Lorentzian Distance Learning for Hyperbolic Representations.
Marc T. Law, Renjie Liao, Jake Snell, and Richard Zemel. ICML 2019.
retrieval code | binary classification code | supplemental
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models.
Marc T. Law, Jake Snell, Amir-Massoud Farahmand, Raquel Urtasun, and Richard Zemel. ICLR 2019.
visualization code | flowers zero-shot code | CUB zero-shot code
Learning Latent Subspaces in Variational Autoencoders.
Jack Klys, Jake Snell, and Richard Zemel. NeurIPS 2018.
Meta-Learning for Semi-Supervised Few-Shot Classification.
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, and Richard Zemel. ICLR 2018.
Stochastic Segmentation Trees for Multiple Ground Truths.
Jake Snell and Richard Zemel. UAI 2017.
Learning to Generate Images with Perceptual Similarity Metrics.
Jake Snell, Karl Ridgeway, Renjie Liao, Brett Roads, Michael C. Mozer, and Richard Zemel. ICIP 2017.
- Learning to Build Probabilistic Models with Limited Data.
Jake Snell. Ph.D. Thesis, Dept. of Computer Science, Univ. of Toronto, 2021.