Variational Learning for Recurrent Spiking Networks. - Danilo Jimenez Rezende; Daan Wierstra; Wulfram Gerstner (NIPS, 2011)

Stochastic Backpropagation and Approximate Inference in Deep Generative Models. - Danilo Jimenez Rezende; Shakir Mohamed; Daan Wierstra (ICML, 2014)

Semi-supervised Learning with Deep Generative Models. - Diederik P. Kingma; Shakir Mohamed; Danilo Jimenez Rezende; Max Welling (NIPS, 2014)

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. - Danilo Jimenez Rezende; Shakir Mohamed; Daan Wierstra (CoRR, 2014)

Stochastic variational learning in recurrent spiking networks. - Danilo Jimenez Rezende; Wulfram Gerstner (Frontiers Comput. Neurosci., 2014)

DRAW: A Recurrent Neural Network For Image Generation. - Karol Gregor; Ivo Danihelka; Alex Graves; Danilo Jimenez Rezende; Daan Wierstra (ICML, 2015)

Variational Inference with Normalizing Flows. - Danilo Jimenez Rezende; Shakir Mohamed (ICML, 2015)

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. - Shakir Mohamed; Danilo Jimenez Rezende (NIPS, 2015)

Towards Principled Unsupervised Learning. - Ilya Sutskever; Rafal Józefowicz; Karol Gregor; Danilo Jimenez Rezende; Timothy P. Lillicrap; Oriol Vinyals (CoRR, 2015)

Variational Inference for Monte Carlo Objectives. - Andriy Mnih; Danilo Jimenez Rezende (ICML, 2016)

One-Shot Generalization in Deep Generative Models. - Danilo Jimenez Rezende; Shakir Mohamed; Ivo Danihelka; Karol Gregor; Daan Wierstra (ICML, 2016)

Interaction Networks for Learning about Objects, Relations and Physics. - Peter W. Battaglia; Razvan Pascanu; Matthew Lai; Danilo Jimenez Rezende; Koray Kavukcuoglu (NIPS, 2016)

Towards Conceptual Compression. - Karol Gregor; Frederic Besse; Danilo Jimenez Rezende; Ivo Danihelka; Daan Wierstra (NIPS, 2016)

Unsupervised Learning of 3D Structure from Images. - Danilo Jimenez Rezende; S. M. Ali Eslami; Shakir Mohamed; Peter W. Battaglia; Max Jaderberg; Nicolas Heess (NIPS, 2016)

Normalizing Flows on Riemannian Manifolds. - Mevlana C. Gemici; Danilo Jimenez Rezende; Shakir Mohamed (CoRR, 2016)

Variational Intrinsic Control. - Karol Gregor; Danilo Jimenez Rezende; Daan Wierstra (ICLR (Workshop), 2017)

Variational Memory Addressing in Generative Models. - Jörg Bornschein; Andriy Mnih; Daniel Zoran; Danilo Jimenez Rezende (NIPS, 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 001; Razvan Pascanu; Peter W. Battaglia; et al. (NIPS, 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. - Theophane Weber; Sébastien Racanière; David P. Reichert; Lars Buesing; Arthur Guez; Danilo Jimenez Rezende; Adrià Puigdomènech Badia; Oriol Vinyals; Nicolas Heess; Yujia Li 001; Razvan Pascanu; Peter W. Battaglia; et al. (CoRR, 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; et al. (CoRR, 2017)

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. - Scott E. Reed; Yutian Chen 001; Thomas Paine; Aäron van den Oord; S. M. Ali Eslami; Danilo J. Rezende; Oriol Vinyals; Nando de Freitas (ICLR (Poster), 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 (ICML, 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 (ICML, 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 (NeurIPS, 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)

Optimizing Slate Recommendations via Slate-CVAE. - Ray Jiang; Sven Gowal; Timothy A. Mann; Danilo J. Rezende (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; et al. (CoRR, 2018)

Neural Processes. - Marta Garnelo; Jonathan Schwarz; Dan Rosenbaum; Fabio Viola; Danilo J. Rezende; S. M. Ali Eslami; Yee Whye Teh (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)

Learning models for visual 3D localization with implicit mapping. - Dan Rosenbaum; Frederic Besse; Fabio Viola; Danilo J. Rezende; S. M. Ali Eslami (CoRR, 2018)

Taming VAEs. - Danilo Jimenez Rezende; Fabio Viola (CoRR, 2018)

Towards a Definition of Disentangled Representations. - Irina Higgins; David Amos; David Pfau; Sébastien Racanière; Loïc Matthey; Danilo J. Rezende; Alexander Lerchner (CoRR, 2018)

Beyond Greedy Ranking: Slate Optimization via List-CVAE. - Ray Jiang; Sven Gowal; Yuqiu Qian; Timothy A. Mann; Danilo J. Rezende (ICLR (Poster), 2019)

Shaping Belief States with Generative Environment Models for RL. - Karol Gregor; Danilo Jimenez Rezende; Frederic Besse; Yan Wu 010; Hamza Merzic; Aäron van den Oord (NeurIPS, 2019)

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. - Alexander Mott; Daniel Zoran; Mike Chrzanowski; Daan Wierstra; Danilo Jimenez Rezende (NeurIPS, 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)

Equivariant Hamiltonian Flows. - Danilo Jimenez Rezende; Sébastien Racanière; Irina Higgins; Peter Toth (CoRR, 2019)

Information bottleneck through variational glasses. - Slava Voloshynovskiy; Mouad Kondah; Shideh Rezaeifar; Olga Taran; Taras Holotyak; Danilo Jimenez Rezende (CoRR, 2019)

Hamiltonian Generative Networks. - Peter Toth; Danilo J. Rezende; Andrew Jaegle; Sébastien Racanière; Aleksandar Botev; Irina Higgins (ICLR, 2020)

Normalizing Flows on Tori and Spheres. - Danilo Jimenez Rezende; George Papamakarios; Sébastien Racanière; Michael S. Albergo; Gurtej Kanwar; Phiala E. Shanahan; Kyle Cranmer (ICML, 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 001; Jovana Mitrovic; Frederic Besse; et al. (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)

Neural Communication Systems with Bandwidth-limited Channel. - Karen Ullrich; Fabio Viola; Danilo Jimenez Rezende (CoRR, 2020)

Conditional Set Generation with Transformers. - Adam R. Kosiorek; Hyunjik Kim; 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)

Amortized learning of neural causal representations. - Nan Rosemary Ke; Jane X. Wang; Jovana Mitrovic; Martin Szummer; Danilo J. Rezende (CoRR, 2020)

Integrable Nonparametric Flows. - David Pfau; Danilo J. Rezende (CoRR, 2020)

Variational Information Bottleneck for Semi-Supervised Classification. - Slava Voloshynovskiy; Olga Taran; Mouad Kondah; Taras Holotyak; Danilo J. Rezende (Entropy, 2020)

PARTS: Unsupervised segmentation with slots, attention and independence maximization. - Daniel Zoran; Rishabh Kabra; Alexander Lerchner; Danilo J. Rezende (ICCV, 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 (ICML, 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 (NeurIPS Datasets and Benchmarks, 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)

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)

Implicit Riemannian Concave Potential Maps. - Danilo J. Rezende; Sébastien Racanière (CoRR, 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)

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 (ICML, 2022)

Continual Repeated Annealed Flow Transport Monte Carlo. - Alexander G. de G. Matthews; Michael Arbel; Danilo Jimenez Rezende; Arnaud Doucet (ICML, 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)

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)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane Wang 001; Jörg Bornschein; Theophane Weber; Anirudh Goyal; Matthew M. Botvinick; Michael Mozer; Danilo Jimenez Rezende (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)

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; et al. (CoRR, 2022)

Symmetry-Based Representations for Artificial and Biological General Intelligence. - Irina Higgins; Sébastien Racanière; Danilo J. Rezende (Frontiers Comput. Neurosci., 2022)

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 (ICLR, 2023)

Combining Behaviors with the Successor Features Keyboard. - Wilka Carvalho; Andre Saraiva 001; Angelos Filos; Andrew K. Lampinen; Loic Matthey; Richard L. Lewis; Honglak Lee; Satinder Singh 001; Danilo Jimenez Rezende; Daniel Zoran (NeurIPS, 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)

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; et al. (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; et al. (CoRR, 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)

Generative Hierarchical Materials Search. - Sherry Yang 001; Simon L. Batzner; Ruiqi Gao; Muratahan Aykol; Alexander L. Gaunt; Brendan McMorrow; Danilo Jimenez Rezende; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (NeurIPS, 2024)

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. - Aniket Didolkar; Anirudh Goyal; Nan Rosemary Ke; Siyuan Guo; Michal Valko; Timothy P. Lillicrap; Danilo Jimenez Rezende; Yoshua Bengio; Michael C. Mozer; Sanjeev Arora (NeurIPS, 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)

Scaling Instructable Agents Across Many Simulated Worlds. - SIMA Team; Maria Abi Raad; Arun Ahuja; Catarina Barros; Frederic Besse; Andrew Bolt; Adrian Bolton; Bethanie Brownfield; Gavin Buttimore; Max Cant; Sarah Chakera; Stephanie C. Y. Chan; et al. (CoRR, 2024)

Can foundation models actively gather information in interactive environments to test hypotheses? - Nan Rosemary Ke; Danny P. Sawyer; Hubert Soyer; Martin Engelcke; David P. Reichert; Drew A. Hudson; John Reid; Alexander Lerchner; Danilo Jimenez Rezende; Timothy P. Lillicrap; Michael Mozer; Jane X. Wang (CoRR, 2024)