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Jake C. Snell
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Year
Prototypical networks for few-shot learning
J Snell, K Swersky, RS Zemel
Advances in Neural Information Processing Systems 30, 4077-4087, 2017
71082017
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
13062018
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
1782017
Learning latent subspaces in variational autoencoders
J Klys, J Snell, R Zemel
Advances in Neural Information Processing Systems 31, 6444-6454, 2018
1292018
Lorentzian distance learning for hyperbolic representations
M Law, R Liao, J Snell, R Zemel
International Conference on Machine Learning, 3672-3681, 2019
732019
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
J Snell, R Zemel
Ninth International Conference on Learning Representations (ICLR 2021), 2021
542021
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
132018
Few-Shot Attribute Learning
M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...
8*2021
Stochastic Segmentation Trees for Multiple Ground Truths.
J Snell, RS Zemel
UAI, 2017
32017
Im-Promptu: In-Context Composition from Image Prompts
B Dedhia, M Chang, JC Snell, TL Griffiths, NK Jha
arXiv preprint arXiv:2305.17262, 2023
2023
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
JC Snell, TP Zollo, Z Deng, T Pitassi, R Zemel
arXiv preprint arXiv:2212.13629, 2022
2022
Learning to Build Probabilistic Models with Limited Data
JC Snell
University of Toronto (Canada), 2021
2021
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Articles 1–12