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ORCIDDanilo Jimenez Rezende
Danilo J. Rezende
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2020 – today
- 2024
[i64]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:
Applications of flow models to the generation of correlated lattice QCD ensembles. CoRR abs/2401.10874 (2024)- [i63]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, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young:
Scaling Instructable Agents Across Many Simulated Worlds. CoRR abs/2404.10179 (2024) - [i62]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora:
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR abs/2405.12205 (2024) - [i61]Sherry Yang, Simon L. Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk:
Generative Hierarchical Materials Search. CoRR abs/2409.06762 (2024) - 2023
- [c30]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 - [c29]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 - [i60]Pol Moreno, Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Rosália G. Schneider, Björn Winckler, Larisa Markeeva, Théophane Weber, Danilo J. Rezende:
Laser: Latent Set Representations for 3D Generative Modeling. CoRR abs/2301.05747 (2023) - [i59]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:
DiscoGen: Learning to Discover Gene Regulatory Networks. CoRR abs/2304.05823 (2023) - [i58]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:
Normalizing flows for lattice gauge theory in arbitrary space-time dimension. CoRR abs/2305.02402 (2023) - [i57]Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan:
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics. CoRR abs/2309.01156 (2023) - [i56]Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew Kyle Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo J. Rezende, Daniel Zoran:
Combining Behaviors with the Successor Features Keyboard. CoRR abs/2310.15940 (2023) - 2022
- [j4]Irina Higgins, Sébastien Racanière, Danilo J. Rezende:
Symmetry-Based Representations for Artificial and Biological General Intelligence. Frontiers Comput. Neurosci. 16: 836498 (2022) - [c28]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: 5694-5725 - [c27]Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. ICML 2022: 15196-15219 - [i55]Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo J. Rezende, Dan Rosenbaum:
From data to functa: Your data point is a function and you should treat it like one. CoRR abs/2201.12204 (2022) - [i54]Alexander G. de G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. CoRR abs/2201.13117 (2022) - [i53]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:
Flow-based sampling in the lattice Schwinger model at criticality. CoRR abs/2202.11712 (2022) - [i52]Irina Higgins, Sébastien Racanière, Danilo J. Rezende:
Symmetry-Based Representations for Artificial and Biological General Intelligence. CoRR abs/2203.09250 (2022) - [i51]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 abs/2204.04875 (2022) - [i50]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:
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions. CoRR abs/2207.08945 (2022) - [i49]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:
Aspects of scaling and scalability for flow-based sampling of lattice QCD. CoRR abs/2211.07541 (2022) - 2021
- [j3]George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan:
Normalizing Flows for Probabilistic Modeling and Inference. J. Mach. Learn. Res. 22: 57:1-57:64 (2021) - [c26]Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende:
PARTS: Unsupervised segmentation with slots, attention and independence maximization. ICCV 2021: 10419-10427 - [c25]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: 5742-5752 - [c24]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 - [i48]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 abs/2101.08176 (2021) - [i47]Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo J. Rezende:
NeRF-VAE: A Geometry Aware 3D Scene Generative Model. CoRR abs/2104.00587 (2021) - [i46]Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan:
Flow-based sampling for fermionic lattice field theories. CoRR abs/2106.05934 (2021) - [i45]Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Christopher J. Pal:
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. CoRR abs/2107.00848 (2021) - [i44]Danilo J. Rezende, Sébastien Racanière:
Implicit Riemannian Concave Potential Maps. CoRR abs/2110.01288 (2021) - 2020
- [j2]Slava Voloshynovskiy, Olga Taran, Mouad Kondah, Taras Holotyak, Danilo J. Rezende:
Variational Information Bottleneck for Semi-Supervised Classification. Entropy 22(9): 943 (2020) - [c23]Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins:
Hamiltonian Generative Networks. ICLR 2020 - [c22]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: 8083-8092 - [i43]Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer:
Normalizing Flows on Tori and Spheres. CoRR abs/2002.02428 (2020) - [i42]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:
Causally Correct Partial Models for Reinforcement Learning. CoRR abs/2002.02836 (2020) - [i41]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 abs/2003.06413 (2020) - [i40]Karen Ullrich, Fabio Viola, Danilo Jimenez Rezende:
Neural Communication Systems with Bandwidth-limited Channel. CoRR abs/2003.13367 (2020) - [i39]Adam R. Kosiorek, Hyunjik Kim, Danilo J. Rezende:
Conditional Set Generation with Transformers. CoRR abs/2006.16841 (2020) - [i38]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 abs/2008.05456 (2020) - [i37]Nan Rosemary Ke, Jane X. Wang, Jovana Mitrovic, Martin Szummer, Danilo J. Rezende:
Amortized learning of neural causal representations. CoRR abs/2008.09301 (2020) - [i36]David Pfau, Danilo J. Rezende:
Integrable Nonparametric Flows. CoRR abs/2012.02035 (2020)
2010 – 2019
- 2019
- [c21]Ray Jiang, Sven Gowal, Yuqiu Qian, Timothy A. Mann, Danilo J. Rezende:
Beyond Greedy Ranking: Slate Optimization via List-CVAE. ICLR (Poster) 2019 - [c20]Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende:
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. NeurIPS 2019: 12329-12338 - [c19]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: 13475-13487 - [i35]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 abs/1905.13077 (2019) - [i34]Alex Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo J. Rezende:
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. CoRR abs/1906.02500 (2019) - [i33]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 abs/1906.09237 (2019) - [i32]Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, Peter Toth:
Equivariant Hamiltonian Flows. CoRR abs/1909.13739 (2019) - [i31]Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins:
Hamiltonian Generative Networks. CoRR abs/1909.13789 (2019) - [i30]Slava Voloshynovskiy, Mouad Kondah, Shideh Rezaeifar, Olga Taran, Taras Holotyak, Danilo Jimenez Rezende:
Information bottleneck through variational glasses. CoRR abs/1912.00830 (2019) - [i29]George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan:
Normalizing Flows for Probabilistic Modeling and Inference. CoRR abs/1912.02762 (2019) - 2018
- [c18]Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo J. Rezende, Oriol Vinyals, Nando de Freitas:
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. ICLR (Poster) 2018 - [c17]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: 1544-1553 - [c16]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: 1690-1699 - [c15]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: 6965-6975 - [i28]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 abs/1802.03006 (2018) - [i27]Ray Jiang, Sven Gowal, Timothy A. Mann, Danilo J. Rezende:
Optimizing Slate Recommendations via Slate-CVAE. CoRR abs/1803.01682 (2018) - [i26]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:
Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR abs/1803.10760 (2018) - [i25]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 abs/1804.09401 (2018) - [i24]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 abs/1806.05034 (2018) - [i23]Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami:
Conditional Neural Processes. CoRR abs/1807.01613 (2018) - [i22]Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh:
Neural Processes. CoRR abs/1807.01622 (2018) - [i21]Ananya Kumar, S. M. Ali Eslami, Danilo J. Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan:
Consistent Generative Query Networks. CoRR abs/1807.02033 (2018) - [i20]Dan Rosenbaum, Frederic Besse, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami:
Learning models for visual 3D localization with implicit mapping. CoRR abs/1807.03149 (2018) - [i19]Danilo Jimenez Rezende, Fabio Viola:
Taming VAEs. CoRR abs/1810.00597 (2018) - [i18]Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner:
Towards a Definition of Disentangled Representations. CoRR abs/1812.02230 (2018) - 2017
- [c14]Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra:
Variational Intrinsic Control. ICLR (Workshop) 2017 - [c13]Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende:
Variational Memory Addressing in Generative Models. NIPS 2017: 3920-3929 - [c12]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: 5690-5701 - [i17]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 abs/1702.04649 (2017) - [i16]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 abs/1707.06203 (2017) - [i15]Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende:
Variational Memory Addressing in Generative Models. CoRR abs/1709.07116 (2017) - [i14]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 abs/1710.10304 (2017) - [i13]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:
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR abs/1711.08378 (2017) - 2016
- [c11]Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra:
One-Shot Generalization in Deep Generative Models. ICML 2016: 1521-1529 - [c10]Andriy Mnih, Danilo Jimenez Rezende:
Variational Inference for Monte Carlo Objectives. ICML 2016: 2188-2196 - [c9]Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra:
Towards Conceptual Compression. NIPS 2016: 3549-3557 - [c8]Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu:
Interaction Networks for Learning about Objects, Relations and Physics. NIPS 2016: 4502-4510 - [c7]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: 4997-5005 - [i12]Andriy Mnih, Danilo Jimenez Rezende:
Variational inference for Monte Carlo objectives. CoRR abs/1602.06725 (2016) - [i11]Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra:
One-Shot Generalization in Deep Generative Models. CoRR abs/1603.05106 (2016) - [i10]Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra:
Towards Conceptual Compression. CoRR abs/1604.08772 (2016) - [i9]Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter W. Battaglia, Max Jaderberg, Nicolas Heess:
Unsupervised Learning of 3D Structure from Images. CoRR abs/1607.00662 (2016) - [i8]Mevlana C. Gemici, Danilo Jimenez Rezende, Shakir Mohamed:
Normalizing Flows on Riemannian Manifolds. CoRR abs/1611.02304 (2016) - [i7]Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra:
Variational Intrinsic Control. CoRR abs/1611.07507 (2016) - [i6]Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu:
Interaction Networks for Learning about Objects, Relations and Physics. CoRR abs/1612.00222 (2016) - 2015
- [c6]Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra:
DRAW: A Recurrent Neural Network For Image Generation. ICML 2015: 1462-1471 - [c5]Danilo Jimenez Rezende, Shakir Mohamed:
Variational Inference with Normalizing Flows. ICML 2015: 1530-1538 - [c4]Shakir Mohamed, Danilo Jimenez Rezende:
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. NIPS 2015: 2125-2133 - [i5]Danilo Jimenez Rezende, Shakir Mohamed:
Variational Inference with Normalizing Flows. CoRR abs/1505.05770 (2015) - [i4]Shakir Mohamed, Danilo Jimenez Rezende:
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. CoRR abs/1509.08731 (2015) - [i3]Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals:
Towards Principled Unsupervised Learning. CoRR abs/1511.06440 (2015) - 2014
- [j1]Danilo Jimenez Rezende, Wulfram Gerstner:
Stochastic variational learning in recurrent spiking networks. Frontiers Comput. Neurosci. 8: 38 (2014) - [c3]Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra:
Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ICML 2014: 1278-1286 - [c2]Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling:
Semi-supervised Learning with Deep Generative Models. NIPS 2014: 3581-3589 - [i2]Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra:
Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. CoRR abs/1401.4082 (2014) - [i1]Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling:
Semi-Supervised Learning with Deep Generative Models. CoRR abs/1406.5298 (2014) - 2011
- [c1]Danilo Jimenez Rezende, Daan Wierstra, Wulfram Gerstner:
Variational Learning for Recurrent Spiking Networks. NIPS 2011: 136-144
Coauthor Index
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last updated on 2024-10-23 21:22 CEST by the dblp team
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