Timezone: »
We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a trained environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several strong baselines.
Author Information
Sébastien Racanière (Google DeepMind)
Sébastien Racanière is a Staff Research Engineer in DeepMind. His current interests in ML revolve around the interaction between Physics and Machine Learning, with an emphasis on the use of symmetries. He got his PhD in pure mathematics from the Université Louis Pasteur, Strasbourg, in 2002, with co-supervisors Michèle Audin (Strasbourg) and Frances Kirwan (Oxford). This was followed by a two years Marie-Curie Individual Fellowship in Imperial College, London, and another postdoc in Cambridge (UK). His first job in the industry was at the Samsung European Research Institute, investigating the use of Learning Algorithms in mobile phones, followed by UGS, a Cambridge based company, working on a 3D search engine. He afterwards worked for Maxeler, in London, programming FPGAs. He then moved to Google, and finally DeepMind.
Theophane Weber (DeepMind)
David Reichert (DeepMind)
Lars Buesing (DeepMind)
Arthur Guez (Google)
Danilo Jimenez Rezende (Google DeepMind)
Adrià Puigdomènech Badia (Google DeepMind)
Oriol Vinyals (Google DeepMind)
Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.
Nicolas Heess (Google DeepMind)
Yujia Li (DeepMind)
Razvan Pascanu (Google DeepMind)
Peter Battaglia (DeepMind)
Demis Hassabis (DeepMind)
David Silver (DeepMind)
Daan Wierstra (DeepMind Technologies)
Related Events (a corresponding poster, oral, or spotlight)
-
2017 Poster: Imagination-Augmented Agents for Deep Reinforcement Learning »
Thu. Dec 7th 02:30 -- 06:30 AM Room Pacific Ballroom #139
More from the Same Authors
-
2020 : Learning Mesh-Based Simulation with Graph Networks »
Tobias Pfaff · Meire Fortunato · Alvaro Sanchez Gonzalez · Peter Battaglia -
2021 : LiRo: Benchmark and leaderboard for Romanian language tasks »
Stefan Dumitrescu · Petru Rebeja · Beata Lorincz · Mihaela Gaman · Andrei Avram · Mihai Ilie · Andrei Pruteanu · Adriana Stan · Lorena Rosia · Cristina Iacobescu · Luciana Morogan · George Dima · Gabriel Marchidan · Traian Rebedea · Madalina Chitez · Dani Yogatama · Sebastian Ruder · Radu Tudor Ionescu · Razvan Pascanu · Viorica Patraucean -
2021 Spotlight: Proper Value Equivalence »
Christopher Grimm · Andre Barreto · Greg Farquhar · David Silver · Satinder Singh -
2021 Spotlight: Online and Offline Reinforcement Learning by Planning with a Learned Model »
Julian Schrittwieser · Thomas Hubert · Amol Mandhane · Mohammadamin Barekatain · Ioannis Antonoglou · David Silver -
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 · Yoshua Bengio · Chris Pal · Michael Mozer -
2021 : Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents »
Jane Wang · Michael King · Nicolas Porcel · Zeb Kurth-Nelson · Tina Zhu · Charles Deck · Peter Choy · Mary Cassin · Malcolm Reynolds · Francis Song · Gavin Buttimore · David Reichert · Neil Rabinowitz · Loic Matthey · Demis Hassabis · Alexander Lerchner · Matt Botvinick -
2021 : Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration »
Oliver Groth · Markus Wulfmeier · Giulia Vezzani · Vibhavari Dasagi · Tim Hertweck · Roland Hafner · Nicolas Heess · Martin Riedmiller -
2021 : Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies »
Dushyant Rao · Fereshteh Sadeghi · Leonard Hasenclever · Markus Wulfmeier · Martina Zambelli · Giulia Vezzani · Dhruva Tirumala · Yusuf Aytar · Josh Merel · Nicolas Heess · Raia Hadsell -
2021 : Implicit Riemannian Concave Potential Maps »
Danilo Jimenez Rezende · Sébastien Racanière -
2021 : Implicit Riemannian Concave Potential Maps »
Danilo Jimenez Rezende · Sébastien Racanière -
2021 : Offline Meta-Reinforcement Learning for Industrial Insertion »
Tony Zhao · Jianlan Luo · Oleg Sushkov · Rugile Pevceviciute · Nicolas Heess · Jonathan Scholz · Stefan Schaal · Sergey Levine -
2022 : Pre-training via Denoising for Molecular Property Prediction »
Sheheryar Zaidi · Michael Schaarschmidt · James Martens · Hyunjik Kim · Yee Whye Teh · Alvaro Sanchez Gonzalez · Peter Battaglia · Razvan Pascanu · Jonathan Godwin -
2022 : When Does Re-initialization Work? »
Sheheryar Zaidi · Tudor Berariu · Hyunjik Kim · Jorg Bornschein · Claudia Clopath · Yee Whye Teh · Razvan Pascanu -
2023 Poster: The Tunnel Effect: Building Data Representations in Deep Neural Networks »
Wojciech Masarczyk · Mateusz Ostaszewski · Ehsan Imani · Razvan Pascanu · Piotr Miłoś · Tomasz Trzcinski -
2023 Poster: Coherent Soft Imitation Learning »
Joe Watson · Sandy Huang · Nicolas Heess -
2023 Poster: Deep Reinforcement Learning with Plasticity Injection »
Evgenii Nikishin · Junhyuk Oh · Georg Ostrovski · Clare Lyle · Razvan Pascanu · Will Dabney · Andre Barreto -
2023 Poster: Learning to Modulate pre-trained Models in RL »
Thomas Schmied · Markus Hofmarcher · Fabian Paischer · Razvan Pascanu · Sepp Hochreiter -
2023 Poster: Discovering Representations for Transfer with Successor Features and the Deep Option Keyboard »
Wilka Carvalho Carvalho · Andre Saraiva · Angelos Filos · Andrew Lampinen · Loic Matthey · Richard L Lewis · Honglak Lee · Satinder Singh · Danilo Jimenez Rezende · Daniel Zoran -
2022 Spotlight: Inverse Design for Fluid-Structure Interactions using Graph Network Simulators »
Kelsey Allen · Tatiana Lopez-Guevara · Kimberly Stachenfeld · Alvaro Sanchez Gonzalez · Peter Battaglia · Jessica Hamrick · Tobias Pfaff -
2022 Spotlight: Lightning Talks 4B-1 »
Alexandra Senderovich · Zhijie Deng · Navid Ansari · Xuefei Ning · Yasmin Salehi · Xiang Huang · Chenyang Wu · Kelsey Allen · Jiaqi Han · Nikita Balagansky · Tatiana Lopez-Guevara · Tianci Li · Zhanhong Ye · Zixuan Zhou · Feng Zhou · Ekaterina Bulatova · Daniil Gavrilov · Wenbing Huang · Dennis Giannacopoulos · Hans-peter Seidel · Anton Obukhov · Kimberly Stachenfeld · Hongsheng Liu · Jun Zhu · Junbo Zhao · Hengbo Ma · Nima Vahidi Ferdowsi · Zongzhang Zhang · Vahid Babaei · Jiachen Li · Alvaro Sanchez Gonzalez · Yang Yu · Shi Ji · Maxim Rakhuba · Tianchen Zhao · Yiping Deng · Peter Battaglia · Josh Tenenbaum · Zidong Wang · Chuang Gan · Changcheng Tang · Jessica Hamrick · Kang Yang · Tobias Pfaff · Yang Li · Shuang Liang · Min Wang · Huazhong Yang · Haotian CHU · Yu Wang · Fan Yu · Bei Hua · Lei Chen · Bin Dong -
2022 Poster: Disentangling Transfer in Continual Reinforcement Learning »
Maciej Wolczyk · Michał Zając · Razvan Pascanu · Łukasz Kuciński · Piotr Miłoś -
2022 Poster: Large-Scale Retrieval for Reinforcement Learning »
Peter Humphreys · Arthur Guez · Olivier Tieleman · Laurent Sifre · Theophane Weber · Timothy Lillicrap -
2022 Poster: An empirical analysis of compute-optimal large language model training »
Jordan Hoffmann · Sebastian Borgeaud · Arthur Mensch · Elena Buchatskaya · Trevor Cai · Eliza Rutherford · Diego de Las Casas · Lisa Anne Hendricks · Johannes Welbl · Aidan Clark · Thomas Hennigan · Eric Noland · Katherine Millican · George van den Driessche · Bogdan Damoc · Aurelia Guy · Simon Osindero · Karén Simonyan · Erich Elsen · Oriol Vinyals · Jack Rae · Laurent Sifre -
2022 Poster: Inverse Design for Fluid-Structure Interactions using Graph Network Simulators »
Kelsey Allen · Tatiana Lopez-Guevara · Kimberly Stachenfeld · Alvaro Sanchez Gonzalez · Peter Battaglia · Jessica Hamrick · Tobias Pfaff -
2022 Poster: Flamingo: a Visual Language Model for Few-Shot Learning »
Jean-Baptiste Alayrac · Jeff Donahue · Pauline Luc · Antoine Miech · Iain Barr · Yana Hasson · Karel Lenc · Arthur Mensch · Katherine Millican · Malcolm Reynolds · Roman Ring · Eliza Rutherford · Serkan Cabi · Tengda Han · Zhitao Gong · Sina Samangooei · Marianne Monteiro · Jacob L Menick · Sebastian Borgeaud · Andy Brock · Aida Nematzadeh · Sahand Sharifzadeh · Mikołaj Bińkowski · Ricardo Barreira · Oriol Vinyals · Andrew Zisserman · Karén Simonyan -
2022 Poster: Data augmentation for efficient learning from parametric experts »
Alexandre Galashov · Josh Merel · Nicolas Heess -
2021 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · David Silver · Matthew Taylor · Martha White · Srijita Das · Yuqing Du · Andrew Patterson · Manan Tomar · Olivia Watkins -
2021 : Bootstrapped Meta-Learning »
Sebastian Flennerhag · Yannick Schroecker · Tom Zahavy · Hado van Hasselt · David Silver · Satinder Singh -
2021 : Implicit Riemannian Concave Potential Maps »
Danilo Jimenez Rezende · Sébastien Racanière -
2021 Poster: Entropic Desired Dynamics for Intrinsic Control »
Steven Hansen · Guillaume Desjardins · Kate Baumli · David Warde-Farley · Nicolas Heess · Simon Osindero · Volodymyr Mnih -
2021 Poster: Powerpropagation: A sparsity inducing weight reparameterisation »
Jonathan Richard Schwarz · Siddhant Jayakumar · Razvan Pascanu · Peter E Latham · Yee Teh -
2021 Poster: Proper Value Equivalence »
Christopher Grimm · Andre Barreto · Greg Farquhar · David Silver · Satinder Singh -
2021 Poster: Neural Production Systems »
Anirudh Goyal · Aniket Didolkar · Nan Rosemary Ke · Charles Blundell · Philippe Beaudoin · Nicolas Heess · Michael Mozer · Yoshua Bengio -
2021 Poster: Continual World: A Robotic Benchmark For Continual Reinforcement Learning »
Maciej Wołczyk · Michał Zając · Razvan Pascanu · Łukasz Kuciński · Piotr Miłoś -
2021 Poster: Discovery of Options via Meta-Learned Subgoals »
Vivek Veeriah · Tom Zahavy · Matteo Hessel · Zhongwen Xu · Junhyuk Oh · Iurii Kemaev · Hado van Hasselt · David Silver · Satinder Singh -
2021 : Live Q&A session: Oriol Vinyals (DeepMind) »
Oriol Vinyals -
2021 : Invited Talk: Oriol Vinyals (DeepMind) »
Oriol Vinyals -
2021 Poster: On the Role of Optimization in Double Descent: A Least Squares Study »
Ilja Kuzborskij · Csaba Szepesvari · Omar Rivasplata · Amal Rannen-Triki · Razvan Pascanu -
2021 Poster: Self-Consistent Models and Values »
Greg Farquhar · Kate Baumli · Zita Marinho · Angelos Filos · Matteo Hessel · Hado van Hasselt · David Silver -
2021 Poster: Online and Offline Reinforcement Learning by Planning with a Learned Model »
Julian Schrittwieser · Thomas Hubert · Amol Mandhane · Mohammadamin Barekatain · Ioannis Antonoglou · David Silver -
2021 Panel: The Consequences of Massive Scaling in Machine Learning »
Noah Goodman · Melanie Mitchell · Joelle Pineau · Oriol Vinyals · Jared Kaplan -
2021 : Symmetries »
Sébastien Racanière -
2021 Tutorial: Pay Attention to What You Need: Do Structural Priors Still Matter in the Age of Billion Parameter Models? »
Irina Higgins · Antonia Creswell · Sébastien Racanière -
2020 : QA: Oriol Vinyals »
Oriol Vinyals -
2020 : Peter Battaglia - Structured models of physics, objects, and scenes »
Peter Battaglia -
2020 : Invited Talk: Oriol Vinyals »
Oriol Vinyals -
2020 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · Joelle Pineau · David Silver · Satinder Singh · Coline Devin · Misha Laskin · Kimin Lee · Janarthanan Rajendran · Vivek Veeriah -
2020 : Peter Battaglia »
Peter Battaglia -
2020 Poster: Top-KAST: Top-K Always Sparse Training »
Siddhant Jayakumar · Razvan Pascanu · Jack Rae · Simon Osindero · Erich Elsen -
2020 Poster: Value-driven Hindsight Modelling »
Arthur Guez · Fabio Viola · Theophane Weber · Lars Buesing · Steven Kapturowski · Doina Precup · David Silver · Nicolas Heess -
2020 Poster: Pointer Graph Networks »
Petar Veličković · Lars Buesing · Matthew Overlan · Razvan Pascanu · Oriol Vinyals · Charles Blundell -
2020 Poster: Discovering Symbolic Models from Deep Learning with Inductive Biases »
Miles Cranmer · Alvaro Sanchez Gonzalez · Peter Battaglia · Rui Xu · Kyle Cranmer · David Spergel · Shirley Ho -
2020 Poster: Critic Regularized Regression »
Ziyu Wang · Alexander Novikov · Konrad Zolna · Josh Merel · Jost Tobias Springenberg · Scott Reed · Bobak Shahriari · Noah Siegel · Caglar Gulcehre · Nicolas Heess · Nando de Freitas -
2020 Spotlight: Pointer Graph Networks »
Petar Veličković · Lars Buesing · Matthew Overlan · Razvan Pascanu · Oriol Vinyals · Charles Blundell -
2020 Session: Orals & Spotlights Track 28: Deep Learning »
Oriol Vinyals · Guido Montufar -
2020 Poster: Understanding the Role of Training Regimes in Continual Learning »
Seyed Iman Mirzadeh · Mehrdad Farajtabar · Razvan Pascanu · Hassan Ghasemzadeh -
2020 Poster: RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning »
Caglar Gulcehre · Ziyu Wang · Alexander Novikov · Thomas Paine · Sergio Gómez · Konrad Zolna · Rishabh Agarwal · Josh Merel · Daniel Mankowitz · Cosmin Paduraru · Gabriel Dulac-Arnold · Jerry Li · Mohammad Norouzi · Matthew Hoffman · Nicolas Heess · Nando de Freitas -
2020 Poster: Disentangling by Subspace Diffusion »
David Pfau · Irina Higgins · Alex Botev · Sébastien Racanière -
2020 Poster: Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces »
Guy Lorberbom · Chris Maddison · Nicolas Heess · Tamir Hazan · Danny Tarlow -
2020 : Keynote Presentation I Oriol Vinyals »
Oriol Vinyals -
2019 : Equivariant Hamiltonian Flows »
Danilo Jimenez Rezende -
2019 : Late-Breaking Papers (Talks) »
David Silver · Simon Du · Matthias Plappert -
2019 : Morning Coffee Break & Poster Session »
Eric Metodiev · Keming Zhang · Markus Stoye · Randy Churchill · Soumalya Sarkar · Miles Cranmer · Johann Brehmer · Danilo Jimenez Rezende · Peter Harrington · AkshatKumar Nigam · Nils Thuerey · Lukasz Maziarka · Alvaro Sanchez Gonzalez · Atakan Okan · James Ritchie · N. Benjamin Erichson · Harvey Cheng · Peihong Jiang · Seong Ho Pahng · Samson Koelle · Sami Khairy · Adrian Pol · Rushil Anirudh · Jannis Born · Benjamin Sanchez-Lengeling · Brian Timar · Rhys Goodall · Tamás Kriváchy · Lu Lu · Thomas Adler · Nathaniel Trask · Noëlie Cherrier · Tomohiko Konno · Muhammad Kasim · Tobias Golling · Zaccary Alperstein · Andrei Ustyuzhanin · James Stokes · Anna Golubeva · Ian Char · Ksenia Korovina · Youngwoo Cho · Chanchal Chatterjee · Tom Westerhout · Gorka Muñoz-Gil · Juan Zamudio-Fernandez · Jennifer Wei · Brian Lee · Johannes Kofler · Bruce Power · Nikita Kazeev · Andrey Ustyuzhanin · Artem Maevskiy · Pascal Friederich · Arash Tavakoli · Willie Neiswanger · Bohdan Kulchytskyy · sindhu hari · Paul Leu · Paul Atzberger -
2019 : Grandmaster Level in StarCraft II using Multi-Agent Reinforcement Learning - Invited Talk »
Oriol Vinyals -
2019 : The MineRL competition »
Misa Ogura · Joe Booth · Sophia Sun · Nicholay Topin · Brandon Houghton · William Guss · Stephanie Milani · Oriol Vinyals · Katja Hofmann · JIA KIM · Karolis Ramanauskas · Florian Laurent · Daichi Nishio · Anssi Kanervisto · Alexey Skrynnik · Artemij Amiranashvili · Christian Scheller · KAIXIN WANG · Yanick Schraner -
2019 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · Joelle Pineau · David Silver · Satinder Singh · Joshua Achiam · Carlos Florensa · Christopher Grimm · Haoran Tang · Vivek Veeriah -
2019 : Closing remarks »
Dan Rosenbaum · Marta Garnelo · Peter Battaglia · Kelsey Allen · Ilker Yildirim -
2019 : Panel »
Sanja Fidler · Josh Tenenbaum · Tatiana López-Guevara · Danilo Jimenez Rezende · Niloy Mitra -
2019 : Danilo Rezende »
Danilo Jimenez Rezende -
2019 : Peter Battaglia: Graph Networks for Learning Physics »
Peter Battaglia -
2019 : Opening Remarks »
Dan Rosenbaum · Marta Garnelo · Peter Battaglia · Kelsey Allen · Ilker Yildirim -
2019 Workshop: Perception as generative reasoning: structure, causality, probability »
Dan Rosenbaum · Marta Garnelo · Peter Battaglia · Kelsey Allen · Ilker Yildirim -
2019 Poster: Learning Transferable Graph Exploration »
Hanjun Dai · Yujia Li · Chenglong Wang · Rishabh Singh · Po-Sen Huang · Pushmeet Kohli -
2019 Poster: Generalization of Reinforcement Learners with Working and Episodic Memory »
Meire Fortunato · Melissa Tan · Ryan Faulkner · Steven Hansen · Adrià Puigdomènech Badia · Gavin Buttimore · Charles Deck · Joel Leibo · Charles Blundell -
2019 Poster: Towards Interpretable Reinforcement Learning Using Attention Augmented Agents »
Alexander Mott · Daniel Zoran · Mike Chrzanowski · Daan Wierstra · Danilo Jimenez Rezende -
2019 Poster: Shaping Belief States with Generative Environment Models for RL »
Karol Gregor · Danilo Jimenez Rezende · Frederic Besse · Yan Wu · Hamza Merzic · Aaron van den Oord -
2019 Poster: Continual Unsupervised Representation Learning »
Dushyant Rao · Francesco Visin · Andrei A Rusu · Razvan Pascanu · Yee Whye Teh · Raia Hadsell -
2019 Poster: Generating Diverse High-Fidelity Images with VQ-VAE-2 »
Ali Razavi · Aaron van den Oord · Oriol Vinyals -
2019 Poster: Classification Accuracy Score for Conditional Generative Models »
Suman Ravuri · Oriol Vinyals -
2019 Poster: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2019 Spotlight: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2019 Poster: Efficient Graph Generation with Graph Recurrent Attention Networks »
Renjie Liao · Yujia Li · Yang Song · Shenlong Wang · Will Hamilton · David Duvenaud · Raquel Urtasun · Richard Zemel -
2018 : Discussion Panel: Ryan Adams, Nicolas Heess, Leslie Kaelbling, Shie Mannor, Emo Todorov (moderator: Roy Fox) »
Ryan Adams · Nicolas Heess · Leslie Kaelbling · Shie Mannor · Emo Todorov · Roy Fox -
2018 : Probabilistic Reasoning for Reinforcement Learning (Nicolas Heess) »
Nicolas Heess -
2018 : David Silver »
David Silver -
2018 : Talk 5: Peter Battaglia - Structure in Physical Intelligence »
Peter Battaglia -
2018 : Introduction of the workshop »
Razvan Pascanu · Yee Teh · Mark Ring · Marc Pickett -
2018 Workshop: Continual Learning »
Razvan Pascanu · Yee Teh · Marc Pickett · Mark Ring -
2018 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · David Silver · Satinder Singh · Joelle Pineau · Joshua Achiam · Rein Houthooft · Aravind Srinivas -
2018 Poster: Single-Agent Policy Tree Search With Guarantees »
Laurent Orseau · Levi Lelis · Tor Lattimore · Theophane Weber -
2018 Poster: A Probabilistic U-Net for Segmentation of Ambiguous Images »
Simon Kohl · Bernardino Romera-Paredes · Clemens Meyer · Jeffrey De Fauw · Joseph R. Ledsam · Klaus Maier-Hein · S. M. Ali Eslami · Danilo Jimenez Rezende · Olaf Ronneberger -
2018 Spotlight: A Probabilistic U-Net for Segmentation of Ambiguous Images »
Simon Kohl · Bernardino Romera-Paredes · Clemens Meyer · Jeffrey De Fauw · Joseph R. Ledsam · Klaus Maier-Hein · S. M. Ali Eslami · Danilo Jimenez Rezende · Olaf Ronneberger -
2018 Poster: Relational recurrent neural networks »
Adam Santoro · Ryan Faulkner · David Raposo · Jack Rae · Mike Chrzanowski · Theophane Weber · Daan Wierstra · Oriol Vinyals · Razvan Pascanu · Timothy Lillicrap -
2017 : Object-oriented intelligence »
Peter Battaglia -
2017 : Panel Discussion »
Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox -
2017 : Meta Unsupervised Learning »
Oriol Vinyals -
2017 : Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière -
2017 : Deep Reinforcement Learning with Subgoals (David Silver) »
David Silver -
2017 Workshop: Deep Learning: Bridging Theory and Practice »
Sanjeev Arora · Maithra Raghu · Russ Salakhutdinov · Ludwig Schmidt · Oriol Vinyals -
2017 : Distilling Expensive Simulations with Neural Networks »
Oriol Vinyals -
2017 Symposium: Deep Reinforcement Learning »
Pieter Abbeel · Yan Duan · David Silver · Satinder Singh · Junhyuk Oh · Rein Houthooft -
2017 Spotlight: Fast amortized inference of neural activity from calcium imaging data with variational autoencoders »
Artur Speiser · Jinyao Yan · Evan Archer · Lars Buesing · Srinivas C Turaga · Jakob H Macke -
2017 Poster: Natural Value Approximators: Learning when to Trust Past Estimates »
Zhongwen Xu · Joseph Modayil · Hado van Hasselt · Andre Barreto · David Silver · Tom Schaul -
2017 Poster: Fast amortized inference of neural activity from calcium imaging data with variational autoencoders »
Artur Speiser · Jinyao Yan · Evan Archer · Lars Buesing · Srinivas C Turaga · Jakob H Macke -
2017 Poster: Successor Features for Transfer in Reinforcement Learning »
Andre Barreto · Will Dabney · Remi Munos · Jonathan Hunt · Tom Schaul · David Silver · Hado van Hasselt -
2017 Poster: A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning »
Marc Lanctot · Vinicius Zambaldi · Audrunas Gruslys · Angeliki Lazaridou · Karl Tuyls · Julien Perolat · David Silver · Thore Graepel -
2017 Poster: Distral: Robust multitask reinforcement learning »
Yee Teh · Victor Bapst · Wojciech Czarnecki · John Quan · James Kirkpatrick · Raia Hadsell · Nicolas Heess · Razvan Pascanu -
2017 Poster: A simple neural network module for relational reasoning »
Adam Santoro · David Raposo · David Barrett · Mateusz Malinowski · Razvan Pascanu · Peter Battaglia · Timothy Lillicrap -
2017 Spotlight: Successor Features for Transfer in Reinforcement Learning »
Andre Barreto · Will Dabney · Remi Munos · Jonathan Hunt · Tom Schaul · David Silver · Hado van Hasselt -
2017 Spotlight: Natural Value Approximators: Learning when to Trust Past Estimates »
Zhongwen Xu · Joseph Modayil · Hado van Hasselt · Andre Barreto · David Silver · Tom Schaul -
2017 Spotlight: A simple neural network module for relational reasoning »
Adam Santoro · David Raposo · David Barrett · Mateusz Malinowski · Razvan Pascanu · Peter Battaglia · Timothy Lillicrap -
2017 Poster: Variational Memory Addressing in Generative Models »
Jörg Bornschein · Andriy Mnih · Daniel Zoran · Danilo Jimenez Rezende -
2017 Poster: Visual Interaction Networks: Learning a Physics Simulator from Video »
Nicholas Watters · Daniel Zoran · Theophane Weber · Peter Battaglia · Razvan Pascanu · Andrea Tacchetti -
2017 Poster: Neural Discrete Representation Learning »
Aaron van den Oord · Oriol Vinyals · koray kavukcuoglu -
2017 Poster: Filtering Variational Objectives »
Chris Maddison · John Lawson · George Tucker · Nicolas Heess · Mohammad Norouzi · Andriy Mnih · Arnaud Doucet · Yee Teh -
2017 Poster: Sobolev Training for Neural Networks »
Wojciech Czarnecki · Simon Osindero · Max Jaderberg · Grzegorz Swirszcz · Razvan Pascanu -
2017 Poster: Robust Imitation of Diverse Behaviors »
Ziyu Wang · Josh Merel · Scott Reed · Nando de Freitas · Gregory Wayne · Nicolas Heess -
2017 Poster: Learning Hierarchical Information Flow with Recurrent Neural Modules »
Danijar Hafner · Alexander Irpan · James Davidson · Nicolas Heess -
2017 Tutorial: Deep Learning: Practice and Trends »
Nando de Freitas · Scott Reed · Oriol Vinyals -
2016 Workshop: Continual Learning and Deep Networks »
Razvan Pascanu · Mark Ring · Tom Schaul -
2016 Poster: Unsupervised Learning of 3D Structure from Images »
Danilo Jimenez Rezende · S. M. Ali Eslami · Shakir Mohamed · Peter Battaglia · Max Jaderberg · Nicolas Heess -
2016 Poster: Conditional Image Generation with PixelCNN Decoders »
Aaron van den Oord · Nal Kalchbrenner · Lasse Espeholt · koray kavukcuoglu · Oriol Vinyals · Alex Graves -
2016 Poster: Attend, Infer, Repeat: Fast Scene Understanding with Generative Models »
S. M. Ali Eslami · Nicolas Heess · Theophane Weber · Yuval Tassa · David Szepesvari · koray kavukcuoglu · Geoffrey E Hinton -
2016 Poster: Learning values across many orders of magnitude »
Hado van Hasselt · Arthur Guez · Arthur Guez · Matteo Hessel · Volodymyr Mnih · David Silver -
2016 Poster: An Online Sequence-to-Sequence Model Using Partial Conditioning »
Navdeep Jaitly · Quoc V Le · Oriol Vinyals · Ilya Sutskever · David Sussillo · Samy Bengio -
2016 Poster: Towards Conceptual Compression »
Karol Gregor · Frederic Besse · Danilo Jimenez Rezende · Ivo Danihelka · Daan Wierstra -
2016 Poster: Interaction Networks for Learning about Objects, Relations and Physics »
Peter Battaglia · Razvan Pascanu · Matthew Lai · Danilo Jimenez Rezende · koray kavukcuoglu -
2016 Poster: Strategic Attentive Writer for Learning Macro-Actions »
Alexander (Sasha) Vezhnevets · Volodymyr Mnih · Simon Osindero · Alex Graves · Oriol Vinyals · John Agapiou · koray kavukcuoglu -
2016 Poster: Matching Networks for One Shot Learning »
Oriol Vinyals · Charles Blundell · Timothy Lillicrap · koray kavukcuoglu · Daan Wierstra -
2015 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · John Schulman · Satinder Singh · David Silver -
2015 Poster: Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) »
Mijung Park · Wittawat Jitkrittum · Ahmad Qamar · Zoltan Szabo · Lars Buesing · Maneesh Sahani -
2015 Poster: Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks »
Samy Bengio · Oriol Vinyals · Navdeep Jaitly · Noam Shazeer -
2015 Poster: Natural Neural Networks »
Guillaume Desjardins · Karen Simonyan · Razvan Pascanu · koray kavukcuoglu -
2015 Poster: Gradient Estimation Using Stochastic Computation Graphs »
John Schulman · Nicolas Heess · Theophane Weber · Pieter Abbeel -
2015 Poster: Pointer Networks »
Oriol Vinyals · Meire Fortunato · Navdeep Jaitly -
2015 Spotlight: Pointer Networks »
Oriol Vinyals · Meire Fortunato · Navdeep Jaitly -
2015 Poster: Learning Continuous Control Policies by Stochastic Value Gradients »
Nicolas Heess · Gregory Wayne · David Silver · Timothy Lillicrap · Tom Erez · Yuval Tassa -
2015 Poster: Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning »
Shakir Mohamed · Danilo Jimenez Rezende -
2015 Poster: Grammar as a Foreign Language »
Oriol Vinyals · Łukasz Kaiser · Terry Koo · Slav Petrov · Ilya Sutskever · Geoffrey Hinton -
2015 Tutorial: Large-Scale Distributed Systems for Training Neural Networks »
Jeff Dean · Oriol Vinyals -
2014 Poster: Recurrent Models of Visual Attention »
Volodymyr Mnih · Nicolas Heess · Alex Graves · koray kavukcuoglu -
2014 Spotlight: Recurrent Models of Visual Attention »
Volodymyr Mnih · Nicolas Heess · Alex Graves · koray kavukcuoglu -
2014 Poster: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization »
Yann N Dauphin · Razvan Pascanu · Caglar Gulcehre · Kyunghyun Cho · Surya Ganguli · Yoshua Bengio -
2014 Poster: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2014 Poster: On the Number of Linear Regions of Deep Neural Networks »
Guido F Montufar · Razvan Pascanu · Kyunghyun Cho · Yoshua Bengio -
2014 Spotlight: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2011 Poster: Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability »
David Reichert · Peggy Series · Amos Storkey -
2011 Spotlight: Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability »
David Reichert · Peggy Series · Amos Storkey -
2010 Poster: Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model »
David Reichert · Peggy Series · Amos Storkey