Danilo Jimenez Rezende

Publication Activity (10 Years)

Years Active: 2011-2023
Publications (10 Years): 56

Top Topics

Generative Model

Causal Discovery

Reinforcement Learning

Variational Inference

Top Venues

Publications

Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende
Learning to Induce Causal Structure. ICLR (2023)
Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew K. Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo Jimenez Rezende, Daniel Zoran
Combining Behaviors with the Successor Features Keyboard. NeurIPS (2023)
Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, Dan Rosenbaum
From data to functa: Your data point is a function and you can treat it like one. ICML (2022)
Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende
Learning to Induce Causal Structure. CoRR (2022)
Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet
Continual Repeated Annealed Flow Transport Monte Carlo. ICML (2022)
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Mozer, Yoshua Bengio, Chris Pal
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. NeurIPS Datasets and Benchmarks (2021)
Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo Jimenez Rezende
NeRF-VAE: A Geometry Aware 3D Scene Generative Model. ICML (2021)
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
Introduction to Normalizing Flows for Lattice Field Theory. CoRR (2021)
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
Normalizing Flows for Probabilistic Modeling and Inference. J. Mach. Learn. Res. 22 (2021)
Karen Ullrich, Fabio Viola, Danilo Jimenez Rezende
Neural Communication Systems with Bandwidth-limited Channel. CoRR (2020)
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
Sampling using SU(N) gauge equivariant flows. CoRR (2020)
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
Normalizing Flows on Tori and Spheres. CoRR (2020)
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
Normalizing Flows on Tori and Spheres. ICML (2020)
Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
Equivariant flow-based sampling for lattice gauge theory. CoRR (2020)
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins
Hamiltonian Generative Networks. CoRR (2019)
Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aäron van den Oord
Shaping Belief States with Generative Environment Models for RL. CoRR (2019)
Slava Voloshynovskiy, Mouad Kondah, Shideh Rezaeifar, Olga Taran, Taras Holotyak, Danilo Jimenez Rezende
Information bottleneck through variational glasses. CoRR (2019)
George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
Normalizing Flows for Probabilistic Modeling and Inference. CoRR (2019)
Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aäron van den Oord
Shaping Belief States with Generative Environment Models for RL. NeurIPS (2019)
Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities. CoRR (2019)
Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. NeurIPS (2019)
Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, Peter Toth
Equivariant Hamiltonian Flows. CoRR (2019)
Danilo Jimenez Rezende, Fabio Viola
Taming VAEs. CoRR (2018)
Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola
Generative Temporal Models with Spatial Memory for Partially Observed Environments. ICML (2018)
Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola
Generative Temporal Models with Spatial Memory for Partially Observed Environments. CoRR (2018)
Lars Buesing, Theophane Weber, Sébastien Racanière, S. M. Ali Eslami, Danilo Jimenez Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, Daan Wierstra
Learning and Querying Fast Generative Models for Reinforcement Learning. CoRR (2018)
Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
A Probabilistic U-Net for Segmentation of Ambiguous Images. CoRR (2018)
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami
Conditional Neural Processes. ICML (2018)
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew Botvinick, Demis Hassabis, Timothy P. Lillicrap
Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR (2018)
Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
A Probabilistic U-Net for Segmentation of Ambiguous Images. NeurIPS (2018)
Sébastien Racanière, Theophane Weber, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, Demis Hassabis, David Silver, Daan Wierstra
Imagination-Augmented Agents for Deep Reinforcement Learning. NIPS (2017)
Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, David Silver, Daan Wierstra
Imagination-Augmented Agents for Deep Reinforcement Learning. CoRR (2017)
Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo Jimenez Rezende, David Amos, Timothy P. Lillicrap
Generative Temporal Models with Memory. CoRR (2017)
Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende
Variational Memory Addressing in Generative Models. NIPS (2017)
Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra
Variational Intrinsic Control. ICLR (Workshop) (2017)
Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo Jimenez Rezende, Oriol Vinyals, Nando de Freitas
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. CoRR (2017)
Matthew Botvinick, David G. T. Barrett, Peter W. Battaglia, Nando de Freitas, Dharshan Kumaran, Joel Z. Leibo, Tim Lillicrap, Joseph Modayil, S. Mohamed, Neil C. Rabinowitz, Danilo Jimenez Rezende, Adam Santoro, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR (2017)
Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende
Variational Memory Addressing in Generative Models. CoRR (2017)
Mevlana C. Gemici, Danilo Jimenez Rezende, Shakir Mohamed
Normalizing Flows on Riemannian Manifolds. CoRR (2016)
Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra
One-Shot Generalization in Deep Generative Models. ICML (2016)
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
Towards Conceptual Compression. CoRR (2016)
Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra
Variational Intrinsic Control. CoRR (2016)
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
Towards Conceptual Compression. NIPS (2016)
Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra
One-Shot Generalization in Deep Generative Models. CoRR (2016)
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter W. Battaglia, Max Jaderberg, Nicolas Heess
Unsupervised Learning of 3D Structure from Images. CoRR (2016)
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter W. Battaglia, Max Jaderberg, Nicolas Heess
Unsupervised Learning of 3D Structure from Images. NIPS (2016)
Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu
Interaction Networks for Learning about Objects, Relations and Physics. NIPS (2016)
Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu
Interaction Networks for Learning about Objects, Relations and Physics. CoRR (2016)
Andriy Mnih, Danilo Jimenez Rezende
Variational Inference for Monte Carlo Objectives. ICML (2016)
Andriy Mnih, Danilo Jimenez Rezende
Variational inference for Monte Carlo objectives. CoRR (2016)
Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals
Towards Principled Unsupervised Learning. CoRR (2015)
Danilo Jimenez Rezende, Shakir Mohamed
Variational Inference with Normalizing Flows. CoRR (2015)
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra
DRAW: A Recurrent Neural Network For Image Generation. ICML (2015)
Shakir Mohamed, Danilo Jimenez Rezende
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. CoRR (2015)
Danilo Jimenez Rezende, Shakir Mohamed
Variational Inference with Normalizing Flows. ICML (2015)
Shakir Mohamed, Danilo Jimenez Rezende
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. NIPS (2015)
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
Semi-supervised Learning with Deep Generative Models. NIPS (2014)
Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling
Semi-Supervised Learning with Deep Generative Models. CoRR (2014)
Danilo Jimenez Rezende, Wulfram Gerstner
Stochastic variational learning in recurrent spiking networks. Frontiers Comput. Neurosci. 8 (2014)
Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. CoRR (2014)
Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ICML (2014)
Danilo Jimenez Rezende, Daan Wierstra, Wulfram Gerstner
Variational Learning for Recurrent Spiking Networks. NIPS (2011)