Jake Snell
Jake Snell
University of Toronto, Vector Institute
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Prototypical networks for few-shot learning
J Snell, K Swersky, RS Zemel
Advances in Neural Information Processing Systems 30, 4077-4087, 2017
20842017
Meta-learning for semi-supervised few-shot classification
M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ...
International Conference on Learning Representations, 2018
3762018
Learning to generate images with perceptual similarity metrics
J Snell, K Ridgeway, R Liao, BD Roads, MC Mozer, RS Zemel
2017 IEEE International Conference on Image Processing (ICIP), 4277-4281, 2017
101*2017
Learning latent subspaces in variational autoencoders
J Klys, J Snell, R Zemel
Advances in Neural Information Processing Systems 31, 6444-6454, 2018
482018
Lorentzian distance learning for hyperbolic representations
M Law, R Liao, J Snell, R Zemel
International Conference on Machine Learning, 3672-3681, 2019
12*2019
Dimensionality reduction for representing the knowledge of probabilistic models
MT Law, J Snell, A Farahmand, R Urtasun, RS Zemel
International Conference on Learning Representations, 2018
92018
Stochastic Segmentation Trees for Multiple Ground Truths.
J Snell, RS Zemel
UAI, 2017
22017
Flexible Few-Shot Learning with Contextual Similarity
M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...
arXiv preprint arXiv:2012.05895, 2020
12020
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
J Snell, R Zemel
arXiv preprint arXiv:2007.10417, 2020
2020
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Articles 1–9