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2020 – today
- 2024
[j1]Brennen Mills
, Martin Masek, Julie Boston, Wyatt de Souza, Jake Snell, Stuart Bender, Matthew B. Thompson, Billy Sung, Sara Hansen:
ParaVerse: co-design of a parachute rehearsal and training virtual-reality enhanced simulator for the Australian Defence Force: combining a generative co-design framework and an agile approach to development. Virtual Real. 28(4): 161 (2024)- [c13]Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths:
Implicit Maximum a Posteriori Filtering via Adaptive Optimization. ICLR 2024 - [c12]Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel:
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. ICLR 2024 - [i13]Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths:
Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases. CoRR abs/2405.19420 (2024) - 2023
- [c11]Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel:
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. ICLR 2023 - [c10]Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. NeurIPS 2023 - [c9]Zhun Deng, Thomas P. Zollo, Jake Snell, Toniann Pitassi, Richard S. Zemel:
Distribution-Free Statistical Dispersion Control for Societal Applications. NeurIPS 2023 - [i12]Bhishma Dedhia, Michael Chang, Jake C. Snell, Thomas L. Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. CoRR abs/2305.17262 (2023) - [i11]Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard S. Zemel:
Distribution-Free Statistical Dispersion Control for Societal Applications. CoRR abs/2309.13786 (2023) - [i10]Gianluca M. Bencomo, Jake C. Snell, Thomas L. Griffiths:
Implicit Maximum a Posteriori Filtering via Adaptive Optimization. CoRR abs/2311.10580 (2023) - [i9]Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard S. Zemel:
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. CoRR abs/2311.13628 (2023) - [i8]Jake C. Snell, Gianluca M. Bencomo, Thomas L. Griffiths:
A Metalearned Neural Circuit for Nonparametric Bayesian Inference. CoRR abs/2311.14601 (2023) - 2022
- [i7]Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel:
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. CoRR abs/2212.13629 (2022) - 2021
- [b1]Jake Snell:
Learning to Build Probabilistic Models with Limited Data. University of Toronto, Canada, 2021 - [c8]Jake Snell, Richard S. Zemel:
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. ICLR 2021 - 2020
- [i6]Jake Snell, Richard S. Zemel:
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. CoRR abs/2007.10417 (2020) - [i5]Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard S. Zemel:
Flexible Few-Shot Learning with Contextual Similarity. CoRR abs/2012.05895 (2020)
2010 – 2019
- 2019
- [c7]Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel:
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. ICLR (Poster) 2019 - [c6]Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel:
Lorentzian Distance Learning for Hyperbolic Representations. ICML 2019: 3672-3681 - 2018
- [c5]Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel:
Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR (Poster) 2018 - [c4]Jack Klys, Jake Snell, Richard S. Zemel:
Learning Latent Subspaces in Variational Autoencoders. NeurIPS 2018: 6445-6455 - [i4]Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel:
Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR abs/1803.00676 (2018) - [i3]Jack Klys, Jake Snell, Richard S. Zemel:
Learning Latent Subspaces in Variational Autoencoders. CoRR abs/1812.06190 (2018) - 2017
- [c3]Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel:
Learning to generate images with perceptual similarity metrics. ICIP 2017: 4277-4281 - [c2]Jake Snell, Kevin Swersky, Richard S. Zemel:
Prototypical Networks for Few-shot Learning. NIPS 2017: 4077-4087 - [c1]Jake Snell, Richard S. Zemel:
Stochastic Segmentation Trees for Multiple Ground Truths. UAI 2017 - [i2]Jake Snell, Kevin Swersky, Richard S. Zemel:
Prototypical Networks for Few-shot Learning. CoRR abs/1703.05175 (2017) - 2015
- [i1]Karl Ridgeway, Jake Snell, Brett Roads, Richard S. Zemel, Michael C. Mozer:
Learning to generate images with perceptual similarity metrics. CoRR abs/1511.06409 (2015)
Coauthor Index
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