[1] Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, and Leonid Sigal. Improved few-shot visual classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
[2] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Improved Contrastive Divergence Training of Energy-Based Models. In Proceedings of the 38th International Conference on Machine Learning, pages 2837-2848. PMLR, July 2021. ISSN: 2640-3498.
[3] Yilun Du and Igor Mordatch. Implicit Generation and Modeling with Energy Based Models. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[4] Nikita Dvornik, Cordelia Schmid, and Julien Mairal. Selecting relevant features from a multidomain representation for few-shot classification. In European Conference on Computer Vision, 2020.
[5] Sven Elflein, Bertrand Charpentier, Daniel Zügner, and Stephan Gunnemann. On Out-of-distribution Detection with Energy-based Models. page 13.
[6] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, pages 1126-1135. PMLR, July 2017. ISSN: 2640-3498.
[7] Chelsea Finn, Kelvin Xu, and Sergey Levine. Probabilistic model-agnostic meta-learning. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, page 9537–9548, Red Hook, NY, USA, 2018. Curran Associates Inc.
[8] Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, and S. M. Ali Eslami. Conditional neural processes. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 1704–1713. PMLR, 10–15 Jul 2018.
[9] Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, and Yee Whye Teh. Neural Processes. Technical Report arXiv:1807.01622, arXiv, July 2018. arXiv:1807.01622 [cs, stat] type: article.
[10] Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner. Meta-learning probabilistic inference for prediction. In International Conference on Learning Representations, 2019.
[11] Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths. Recasting gradient-based meta-learning as hierarchical bayes. In International Conference on Learning Representations, 2018.
[12] Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky. Your classifier is secretly an energy based model and you should treat it like one. In International Conference on Learning Representations, 2020.
[13] Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, and David Duvenaud. No {mcmc} for me: Amortized sampling for fast and stable training of energy-based models. In International Conference on Learning Representations, 2021.
[14] Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu. Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7975–7984, Seattle, WA, USA, June 2020. IEEE.
[15] Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Xiaodong Song. Scaling out-of-distribution detection for real-world settings. In International Conference on Machine Learning, 2022.