Danilo Jimenez Rezende

According to our database, Danilo Jimenez Rezende authored at least 71 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Applications of flow models to the generation of correlated lattice QCD ensembles.

Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2024

2023

Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics.

Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan

CoRR, 2023

Normalizing flows for lattice gauge theory in arbitrary space-time dimension.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2023

DiscoGen: Learning to Discover Gene Regulatory Networks.

Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende

CoRR, 2023

Laser: Latent Set Representations for 3D Generative Modeling.

Pol Moreno, Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Rosália G. Schneider, Björn Winckler, Larisa Markeeva, Théophane Weber, Danilo J. Rezende

CoRR, 2023

Combining Behaviors with the Successor Features Keyboard.

Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew K. Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo Jimenez Rezende, Daniel Zoran

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning to Induce Causal Structure.

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Symmetry-Based Representations for Artificial and Biological General Intelligence.

Irina Higgins, Sébastien Racanière, Danilo J. Rezende

Frontiers Comput. Neurosci., 2022

Aspects of scaling and scalability for flow-based sampling of lattice QCD.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2022

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions.

Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban

CoRR, 2022

Learning to Induce Causal Structure.

Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende

CoRR, 2022

Flow-based sampling in the lattice Schwinger model at criticality.

Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2022

From data to functa: Your data point is a function and you should treat it like one.

Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo J. Rezende, Dan Rosenbaum

CoRR, 2022

Continual Repeated Annealed Flow Transport Monte Carlo.

Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet

Proceedings of the International Conference on Machine Learning, 2022

From data to functa: Your data point is a function and you can treat it like one.

Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, Dan Rosenbaum

Proceedings of the International Conference on Machine Learning, 2022

2021

Normalizing Flows for Probabilistic Modeling and Inference.

George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

J. Mach. Learn. Res., 2021

Implicit Riemannian Concave Potential Maps.

Danilo J. Rezende, Sébastien Racanière

CoRR, 2021

Flow-based sampling for fermionic lattice field theories.

Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

CoRR, 2021

Introduction to Normalizing Flows for Lattice Field Theory.

Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

CoRR, 2021

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning.

Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Mozer, Yoshua Bengio, Chris Pal

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

NeRF-VAE: A Geometry Aware 3D Scene Generative Model.

Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo Jimenez Rezende

Proceedings of the 38th International Conference on Machine Learning, 2021

PARTS: Unsupervised segmentation with slots, attention and independence maximization.

Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Variational Information Bottleneck for Semi-Supervised Classification.

Slava Voloshynovskiy, Olga Taran, Mouad Kondah, Taras Holotyak, Danilo J. Rezende

Entropy, 2020

Integrable Nonparametric Flows.

David Pfau, Danilo J. Rezende

CoRR, 2020

Amortized learning of neural causal representations.

Nan Rosemary Ke, Jane X. Wang, Jovana Mitrovic, Martin Szummer, Danilo J. Rezende

CoRR, 2020

Sampling using SU(N) gauge equivariant flows.

Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

CoRR, 2020

Conditional Set Generation with Transformers.

Adam R. Kosiorek, Hyunjik Kim, Danilo J. Rezende

CoRR, 2020

Neural Communication Systems with Bandwidth-limited Channel.

Karen Ullrich, Fabio Viola, Danilo Jimenez Rezende

CoRR, 2020

Equivariant flow-based sampling for lattice gauge theory.

Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

CoRR, 2020

Causally Correct Partial Models for Reinforcement Learning.

Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing

CoRR, 2020

Normalizing Flows on Tori and Spheres.

Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer

Proceedings of the 37th International Conference on Machine Learning, 2020

Hamiltonian Generative Networks.

Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Information bottleneck through variational glasses.

Slava Voloshynovskiy, Mouad Kondah, Shideh Rezaeifar, Olga Taran, Taras Holotyak, Danilo Jimenez Rezende

CoRR, 2019

Equivariant Hamiltonian Flows.

Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, Peter Toth

CoRR, 2019

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities.

Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger

CoRR, 2019

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents.

Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Shaping Belief States with Generative Environment Models for RL.

Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aäron van den Oord

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Beyond Greedy Ranking: Slate Optimization via List-CVAE.

Ray Jiang, Sven Gowal, Yuqiu Qian, Timothy A. Mann, Danilo J. Rezende

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Towards a Definition of Disentangled Representations.

Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner

CoRR, 2018

Taming VAEs.

Danilo Jimenez Rezende, Fabio Viola

CoRR, 2018

Learning models for visual 3D localization with implicit mapping.

Dan Rosenbaum, Frederic Besse, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami

CoRR, 2018

Consistent Generative Query Networks.

Ananya Kumar, S. M. Ali Eslami, Danilo J. Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

CoRR, 2018

Neural Processes.

Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh

CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.

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 M. Botvinick, Demis Hassabis, Timothy P. Lillicrap

CoRR, 2018

Optimizing Slate Recommendations via Slate-CVAE.

Ray Jiang, Sven Gowal, Timothy A. Mann, Danilo J. Rezende

CoRR, 2018

Learning and Querying Fast Generative Models for Reinforcement Learning.

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

CoRR, 2018

A Probabilistic U-Net for Segmentation of Ambiguous Images.

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Conditional Neural Processes.

Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami

Proceedings of the 35th International Conference on Machine Learning, 2018

Generative Temporal Models with Spatial Memory for Partially Observed Environments.

Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola

Proceedings of the 35th International Conference on Machine Learning, 2018

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions.

Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo J. Rezende, Oriol Vinyals, Nando de Freitas

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.

Matthew M. 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

CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

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

CoRR, 2017

Generative Temporal Models with Memory.

Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo Jimenez Rezende, David Amos, Timothy P. Lillicrap

CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Memory Addressing in Generative Models.

Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Intrinsic Control.

Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Normalizing Flows on Riemannian Manifolds.

Mevlana C. Gemici, Danilo Jimenez Rezende, Shakir Mohamed

CoRR, 2016

Unsupervised Learning of 3D Structure from Images.

Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter W. Battaglia, Max Jaderberg, Nicolas Heess

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Towards Conceptual Compression.

Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Interaction Networks for Learning about Objects, Relations and Physics.

Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

One-Shot Generalization in Deep Generative Models.

Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra

Proceedings of the 33nd International Conference on Machine Learning, 2016

Variational Inference for Monte Carlo Objectives.

Andriy Mnih, Danilo Jimenez Rezende

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Towards Principled Unsupervised Learning.

Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals

CoRR, 2015

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.

Shakir Mohamed, Danilo Jimenez Rezende

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Variational Inference with Normalizing Flows.

Danilo Jimenez Rezende, Shakir Mohamed

Proceedings of the 32nd International Conference on Machine Learning, 2015

DRAW: A Recurrent Neural Network For Image Generation.

Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Stochastic variational learning in recurrent spiking networks.

Danilo Jimenez Rezende, Wulfram Gerstner

Frontiers Comput. Neurosci., 2014

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models.

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

CoRR, 2014

Semi-supervised Learning with Deep Generative Models.

Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Stochastic Backpropagation and Approximate Inference in Deep Generative Models.

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

Proceedings of the 31th International Conference on Machine Learning, 2014

2011

Variational Learning for Recurrent Spiking Networks.

Danilo Jimenez Rezende, Daan Wierstra, Wulfram Gerstner

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011