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
Zhun DengIncoming Assistant Professor, CS@UNC Chapel Hill; Postdoc, Columbia UniversityVerified email at columbia.edu
Tom ZolloPhD Candidate, Columbia UniversityVerified email at columbia.edu
James LucasResearch Scientist, NVIDIAVerified email at cs.toronto.edu
Xaq PitkowAssociate Professor of Computational Neuroscience, Carnegie Mellon UniversityVerified email at rice.edu
Andreas ToliasStanford UniversityVerified email at stanford.edu
Kuan-Chieh WangSnap Inc.Verified email at stanford.edu
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
Raquel UrtasunFounder & CEO Waabi, Professor, University of Toronto.Verified email at cs.toronto.edu

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	9528	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	1632	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	221	2017
Learning latent subspaces in variational autoencoders J Klys, J Snell, R Zemel Advances in Neural Information Processing Systems 31, 6444-6454, 2018	163	2018
Lorentzian distance learning for hyperbolic representations M Law, R Liao, J Snell, R Zemel International Conference on Machine Learning, 3672-3681, 2019	112	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	70	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	15	2018
Few-Shot Attribute Learning M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...	13*	2020
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	7	2022
Distribution-free statistical dispersion control for societal applications Z Deng, T Zollo, J Snell, T Pitassi, R Zemel Advances in Neural Information Processing Systems 36, 2024	4	2024
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	4	2023
Stochastic Segmentation Trees for Multiple Ground Truths. J Snell, RS Zemel UAI, 2017	3	2017
Implicit maximum a posteriori filtering via adaptive optimization GM Bencomo, JC Snell, TL Griffiths arXiv preprint arXiv:2311.10580, 2023	2	2023
Im-promptu: in-context composition from image prompts B Dedhia, M Chang, J Snell, T Griffiths, N Jha Advances in Neural Information Processing Systems 36, 2024	1	2024
A Metalearned Neural Circuit for Nonparametric Bayesian Inference JC Snell, G Bencomo, TL Griffiths arXiv preprint arXiv:2311.14601, 2023	1	2023
Improving Predictor Reliability with Selective Recalibration TP Zollo, Z Deng, JC Snell, T Pitassi, R Zemel arXiv preprint arXiv:2410.05407, 2024		2024
Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases R Marjieh, S Kumar, D Campbell, L Zhang, G Bencomo, J Snell, ... arXiv preprint arXiv:2405.19420, 2024		2024
Learning to Build Probabilistic Models with Limited Data JC Snell University of Toronto (Canada), 2021		2021
Early Exiting in Deep Neural Networks via Dirichlet-based Uncertainty Quantification F Xia, J Snell, TL Griffiths NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles …, 0
Binary climate data heightens perceived impact of climate change G Liu, JC Snell, TL Griffiths, R Dubey

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