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We propose a framework called HyperVAE for encoding distributions of distributions.
Keeping neural networks simple by minimizing the description length of the weights
Geoffrey Hinton and Drew Van Camp · 1993
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2016
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Matrix-centric neural networks
Kien Do, Truyen Tran, and Svetha Venkatesh · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, and Aaron Courville · 2017
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Jakub M Tomczak and Max Welling · 2017
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Learning deep matrix representations
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Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm
Chelsea Finn and Sergey Levine · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2018
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Adversarial distillation of bayesian neural network posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, and Richard Zemel · 2018
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Bayesian model-agnostic meta-learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Meta-amortized variational inference and learning
Kristy Choi, Mike Wu, Noah Goodman, and Stefano Ermon · 2019
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Incomplete conditional density estimation for fast materials discovery
Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Matthew Barnett, and Svetha Venkatesh · 2019
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Continual unsupervised representation learning
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
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HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Neale Ratzlaff and Li Fuxin · 2019
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Practical lossless compression with latent variables using bits back coding
James Townsend, Tom Bird, and David Barber · 2019
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