Jake C. Snell

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Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
Eleni TriantafillouGoogle DeepMindVerified email at google.com
Mengye RenNYUVerified email at nyu.edu
Joshua B. TenenbaumMITVerified email at mit.edu
Hugo LarochelleGoogle DeepMind & MilaVerified email at google.com
Sachin RaviAppleVerified email at alumni.princeton.edu
Renjie LiaoUniversity of British ColumbiaVerified email at ece.ubc.ca
Marc T. LawResearch Scientist at NVIDIAVerified email at nvidia.com
Michael MozerGoogle DeepMind; University of ColoradoVerified email at colorado.edu
Brett David RoadsPostdoctoral Research Associate, University College LondonVerified email at ucl.ac.uk
Karl RidgewayFacebookVerified email at fb.com
James LucasResearch Scientist, NVIDIAVerified email at cs.toronto.edu
Xaq PitkowAssociate Professor of Computational Neuroscience, Carnegie Mellon UniversityVerified email at rice.edu
Andreas ToliasDepartment of Neuroscience, Baylor College of MedicineVerified email at cns.bcm.edu
Kuan-Chieh (Jackson) WangStanford UniversityVerified email at stanford.edu
Zhun DengPostdoctoral Researcher, Columbia UniversityVerified email at columbia.edu
Tom ZolloPhD Candidate, Columbia UniversityVerified email at columbia.edu
Raquel UrtasunFounder & CEO Waabi, Professor, University of Toronto.Verified email at cs.toronto.edu
Amir-massoud FarahmandVector Institute, University of TorontoVerified email at vectorinstitute.ai

Jake C. Snell

Princeton University

Verified email at princeton.edu - Homepage

Deep Learning Meta-Learning Bayesian Nonparametrics Few-shot Learning Uncertainty Quantification


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Prototypical networks for few-shot learning J Snell, K Swersky, RS Zemel Advances in Neural Information Processing Systems 30, 4077-4087, 2017	7555	2017
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	1368	2018
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	183	2017
Learning latent subspaces in variational autoencoders J Klys, J Snell, R Zemel Advances in Neural Information Processing Systems 31, 6444-6454, 2018	139	2018
Lorentzian distance learning for hyperbolic representations M Law, R Liao, J Snell, R Zemel International Conference on Machine Learning, 3672-3681, 2019	78	2019
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	57	2021
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	13	2018
Few-Shot Attribute Learning M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...	8*	2021
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	3	2022
Stochastic Segmentation Trees for Multiple Ground Truths. J Snell, RS Zemel UAI, 2017	3	2017
Distribution-Free Statistical Dispersion Control for Societal Applications Z Deng, TP Zollo, JC Snell, T Pitassi, R Zemel arXiv preprint arXiv:2309.13786, 2023	1	2023
A Metalearned Neural Circuit for Nonparametric Bayesian Inference JC Snell, G Bencomo, TL Griffiths arXiv preprint arXiv:2311.14601, 2023		2023
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models TP Zollo, T Morrill, Z Deng, JC Snell, T Pitassi, R Zemel arXiv preprint arXiv:2311.13628, 2023		2023
Implicit Maximum a Posteriori Filtering via Adaptive Optimization GM Bencomo, JC Snell, TL Griffiths arXiv preprint arXiv:2311.10580, 2023		2023
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
Learning to Build Probabilistic Models with Limited Data JC Snell University of Toronto (Canada), 2021		2021

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