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2020 – today
- 2023
[c68]Matko Bosnjak, Pierre Harvey Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic:
SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations. ICLR 2023- [c67]Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin:
Pre-training via Denoising for Molecular Property Prediction. ICLR 2023 - [c66]Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney:
Understanding Plasticity in Neural Networks. ICML 2023: 23190-23211 - [c65]Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De:
Resurrecting Recurrent Neural Networks for Long Sequences. ICML 2023: 26670-26698 - [i96]Matko Bosnjak, Pierre H. Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic:
SemPPL: Predicting pseudo-labels for better contrastive representations. CoRR abs/2301.05158 (2023) - [i95]Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney:
Understanding plasticity in neural networks. CoRR abs/2303.01486 (2023) - [i94]Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De:
Resurrecting Recurrent Neural Networks for Long Sequences. CoRR abs/2303.06349 (2023) - [i93]Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc'Aurelio Ranzato, Razvan Pascanu:
Towards Compute-Optimal Transfer Learning. CoRR abs/2304.13164 (2023) - [i92]Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto:
Deep Reinforcement Learning with Plasticity Injection. CoRR abs/2305.15555 (2023) - [i91]Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani, Razvan Pascanu, Piotr Milos, Tomasz Trzcinski:
The Tunnel Effect: Building Data Representations in Deep Neural Networks. CoRR abs/2305.19753 (2023) - [i90]Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein:
Kalman Filter for Online Classification of Non-Stationary Data. CoRR abs/2306.08448 (2023) - [i89]Thomas Schmied, Markus Hofmarcher, Fabian Paischer, Razvan Pascanu, Sepp Hochreiter:
Learning to Modulate pre-trained Models in RL. CoRR abs/2306.14884 (2023) - [i88]Andrew Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic:
Asynchronous Algorithmic Alignment with Cocycles. CoRR abs/2306.15632 (2023) - [i87]Adam Fisch, Amal Rannen-Triki, Razvan Pascanu, Jörg Bornschein, Angeliki Lazaridou, Elena Gribovskaya, Marc'Aurelio Ranzato:
Towards Robust and Efficient Continual Language Learning. CoRR abs/2307.05741 (2023) - [i86]Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic:
Latent Space Representations of Neural Algorithmic Reasoners. CoRR abs/2307.08874 (2023) - [i85]Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar:
Promoting Exploration in Memory-Augmented Adam using Critical Momenta. CoRR abs/2307.09638 (2023) - [i84]Antonio Orvieto, Soham De, Çaglar Gülçehre, Razvan Pascanu, Samuel L. Smith:
On the Universality of Linear Recurrences Followed by Nonlinear Projections. CoRR abs/2307.11888 (2023) - [i83]Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Max Vladymyrov, Razvan Pascanu, João Sacramento:
Uncovering mesa-optimization algorithms in Transformers. CoRR abs/2309.05858 (2023) - [i82]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. CoRR abs/2311.11908 (2023) - 2022
- [j6]Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess:
Behavior Priors for Efficient Reinforcement Learning. J. Mach. Learn. Res. 23: 221:1-221:68 (2022) - [j5]Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet:
An empirical study of implicit regularization in deep offline RL. Trans. Mach. Learn. Res. 2022 (2022) - [c64]Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi:
Test Sample Accuracy Scales with Training Sample Density in Neural Networks. CoLLAs 2022: 629-646 - [c63]Andrei Alex Rusu, Sebastian Flennerhag, Dushyant Rao, Razvan Pascanu, Raia Hadsell:
Probing Transfer in Deep Reinforcement Learning without Task Engineering. CoLLAs 2022: 1231-1254 - [c62]Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu:
When Does Re-initialization Work? ICBINB 2022: 12-26 - [c61]Seyed-Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Görür, Mehrdad Farajtabar:
Wide Neural Networks Forget Less Catastrophically. ICML 2022: 15699-15717 - [c60]Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell:
The CLRS Algorithmic Reasoning Benchmark. ICML 2022: 22084-22102 - [c59]Florina-Cristina Calnegru
, John Shawe-Taylor, Iasonas Kokkinos, Razvan Pascanu:
Correlation Based Semantic Transfer with Application to Domain Adaptation. ICONIP (1) 2022: 588-599 - [c58]Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell:
Reasoning-Modulated Representations. LoG 2022: 50 - [c57]Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov:
The First Learning on Graphs Conference: Preface. LoG 2022: i-xxiii - [c56]Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Disentangling Transfer in Continual Reinforcement Learning. NeurIPS 2022 - [e2]Sarath Chandar, Razvan Pascanu, Doina Precup:
Conference on Lifelong Learning Agents, CoLLAs 2022, 22-24 August 2022, McGill University, Montréal, Québec, Canada. Proceedings of Machine Learning Research 199, PMLR 2022 [contents] - [e1]Bastian Rieck, Razvan Pascanu:
Learning on Graphs Conference, LoG 2022, 9-12 December 2022, Virtual Event. Proceedings of Machine Learning Research 198, PMLR 2022 [contents] - [i81]Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic:
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? CoRR abs/2201.05119 (2022) - [i80]Seyed-Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Görür, Mehrdad Farajtabar:
Architecture Matters in Continual Learning. CoRR abs/2202.00275 (2022) - [i79]Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell:
The CLRS Algorithmic Reasoning Benchmark. CoRR abs/2205.15659 (2022) - [i78]Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin:
Pre-training via Denoising for Molecular Property Prediction. CoRR abs/2206.00133 (2022) - [i77]Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu:
When Does Re-initialization Work? CoRR abs/2206.10011 (2022) - [i76]Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matt Hoffman, Razvan Pascanu, Arnaud Doucet:
An Empirical Study of Implicit Regularization in Deep Offline RL. CoRR abs/2207.02099 (2022) - [i75]Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Disentangling Transfer in Continual Reinforcement Learning. CoRR abs/2209.13900 (2022) - [i74]Andrei A. Rusu, Sebastian Flennerhag, Dushyant Rao, Razvan Pascanu, Raia Hadsell:
Probing Transfer in Deep Reinforcement Learning without Task Engineering. CoRR abs/2210.12448 (2022) - [i73]Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuang Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de Las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc'Aurelio Ranzato:
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. CoRR abs/2211.11747 (2022) - 2021
- [c55]Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Dilan Görür, Razvan Pascanu, Hassan Ghasemzadeh:
Linear Mode Connectivity in Multitask and Continual Learning. ICLR 2021 - [c54]Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu:
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective. ICML 2021: 3734-3744 - [c53]Stefan Daniel Dumitrescu, Petru Rebeja, Beáta Lorincz, Mihaela Gaman, Andrei-Marius 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:
LiRo: Benchmark and leaderboard for Romanian language tasks. NeurIPS Datasets and Benchmarks 2021 - [c52]Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Continual World: A Robotic Benchmark For Continual Reinforcement Learning. NeurIPS 2021: 28496-28510 - [c51]Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh:
Powerpropagation: A sparsity inducing weight reparameterisation. NeurIPS 2021: 28889-28903 - [c50]Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu:
On the Role of Optimization in Double Descent: A Least Squares Study. NeurIPS 2021: 29567-29577 - [i72]Çaglar Gülçehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew W. Hoffman, Razvan Pascanu, Nando de Freitas:
Regularized Behavior Value Estimation. CoRR abs/2103.09575 (2021) - [i71]Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu:
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective. CoRR abs/2105.05246 (2021) - [i70]Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos:
Continual World: A Robotic Benchmark For Continual Reinforcement Learning. CoRR abs/2105.10919 (2021) - [i69]Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, Samuel L. Smith:
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error. CoRR abs/2105.13343 (2021) - [i68]Tudor Berariu, Wojciech Czarnecki, Soham De, Jörg Bornschein, Samuel L. Smith, Razvan Pascanu, Claudia Clopath:
A study on the plasticity of neural networks. CoRR abs/2106.00042 (2021) - [i67]Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, Erich Elsen:
Top-KAST: Top-K Always Sparse Training. CoRR abs/2106.03517 (2021) - [i66]Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio:
Predicting Unreliable Predictions by Shattering a Neural Network. CoRR abs/2106.08365 (2021) - [i65]Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu:
Task-agnostic Continual Learning with Hybrid Probabilistic Models. CoRR abs/2106.12772 (2021) - [i64]Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell:
Reasoning-Modulated Representations. CoRR abs/2107.08881 (2021) - [i63]Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu:
On the Role of Optimization in Double Descent: A Least Squares Study. CoRR abs/2107.12685 (2021) - [i62]Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh:
Powerpropagation: A sparsity inducing weight reparameterisation. CoRR abs/2110.00296 (2021) - [i61]Seyed-Iman Mirzadeh, Arslan Chaudhry, Huiyi Hu, Razvan Pascanu, Dilan Görür, Mehrdad Farajtabar:
Wide Neural Networks Forget Less Catastrophically. CoRR abs/2110.11526 (2021) - 2020
- [c49]Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, Raia Hadsell:
Meta-Learning with Warped Gradient Descent. ICLR 2020 - [c48]Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack W. Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu:
Multiplicative Interactions and Where to Find Them. ICLR 2020 - [c47]Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh:
Functional Regularisation for Continual Learning with Gaussian Processes. ICLR 2020 - [c46]Albert Gu, Çaglar Gülçehre, Thomas Paine, Matt Hoffman, Razvan Pascanu:
Improving the Gating Mechanism of Recurrent Neural Networks. ICML 2020: 3800-3809 - [c45]Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Çaglar Gülçehre, Siddhant M. Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell:
Stabilizing Transformers for Reinforcement Learning. ICML 2020: 7487-7498 - [c44]Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, Erich Elsen:
Top-KAST: Top-K Always Sparse Training. NeurIPS 2020 - [c43]Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh:
Understanding the Role of Training Regimes in Continual Learning. NeurIPS 2020 - [c42]Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. NeurIPS 2020 - [i60]Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. CoRR abs/2006.06380 (2020) - [i59]Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh:
Understanding the Role of Training Regimes in Continual Learning. CoRR abs/2006.06958 (2020) - [i58]Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu:
Temporal Difference Uncertainties as a Signal for Exploration. CoRR abs/2010.02255 (2020) - [i57]Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Dilan Görür, Razvan Pascanu, Hassan Ghasemzadeh:
Linear Mode Connectivity in Multitask and Continual Learning. CoRR abs/2010.04495 (2020) - [i56]Pierre H. Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel L. Smith, Soham De, Razvan Pascanu, Bilal Piot, Michal Valko:
BYOL works even without batch statistics. CoRR abs/2010.10241 (2020) - [i55]Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess:
Behavior Priors for Efficient Reinforcement Learning. CoRR abs/2010.14274 (2020)
2010 – 2019
- 2019
- [c41]Wojciech M. Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg:
Distilling Policy Distillation. AISTATS 2019: 1331-1340 - [c40]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. DGS@ICLR 2019 - [c39]Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess:
Information asymmetry in KL-regularized RL. ICLR (Poster) 2019 - [c38]Çaglar Gülçehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter W. Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas:
Hyperbolic Attention Networks. ICLR (Poster) 2019 - [c37]Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell:
Meta-Learning with Latent Embedding Optimization. ICLR (Poster) 2019 - [c36]Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia:
Deep reinforcement learning with relational inductive biases. ICLR (Poster) 2019 - [c35]Dushyant Rao, Francesco Visin, Andrei A. Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell:
Continual Unsupervised Representation Learning. NeurIPS 2019: 7645-7655 - [i54]Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh:
Functional Regularisation for Continual Learning using Gaussian Processes. CoRR abs/1901.11356 (2019) - [i53]Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg:
Distilling Policy Distillation. CoRR abs/1902.02186 (2019) - [i52]Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess:
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL. CoRR abs/1903.07438 (2019) - [i51]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. CoRR abs/1903.07714 (2019) - [i50]Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu:
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. CoRR abs/1904.11455 (2019) - [i49]Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess:
Information asymmetry in KL-regularized RL. CoRR abs/1905.01240 (2019) - [i48]Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i47]Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu:
Task Agnostic Continual Learning via Meta Learning. CoRR abs/1906.05201 (2019) - [i46]Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Hujun Yin, Raia Hadsell:
Meta-Learning with Warped Gradient Descent. CoRR abs/1909.00025 (2019) - [i45]Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Çaglar Gülçehre, Siddhant M. Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell:
Stabilizing Transformers for Reinforcement Learning. CoRR abs/1910.06764 (2019) - [i44]Albert Gu, Çaglar Gülçehre, Tom Le Paine, Matthew W. Hoffman, Razvan Pascanu:
Improving the Gating Mechanism of Recurrent Neural Networks. CoRR abs/1910.09890 (2019) - [i43]Dushyant Rao, Francesco Visin, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu, Raia Hadsell:
Continual Unsupervised Representation Learning. CoRR abs/1910.14481 (2019) - [i42]Wojciech Marian Czarnecki, Simon Osindero, Razvan Pascanu, Max Jaderberg:
A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern. CoRR abs/1912.07559 (2019) - 2018
- [j4]Andrea Banino, Caswell Barry, Benigno Uria, Charles Blundell, Timothy P. Lillicrap, Piotr Mirowski, Alexander Pritzel, Martin J. Chadwick, Thomas Degris, Joseph Modayil, Greg Wayne, Hubert Soyer, Fabio Viola, Brian Zhang, Ross Goroshin, Neil C. Rabinowitz, Razvan Pascanu, Charlie Beattie, Stig Petersen, Amir Sadik, Stephen Gaffney, Helen King, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Dharshan Kumaran:
Vector-based navigation using grid-like representations in artificial agents. Nat. 557(7705): 429-433 (2018) - [c34]Antonio Polino, Razvan Pascanu, Dan Alistarh:
Model compression via distillation and quantization. ICLR (Poster) 2018 - [c33]Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae, Alexander Pritzel, Adrià Puigdomènech Badia, Benigno Uria, Oriol Vinyals, Demis Hassabis, Razvan Pascanu, Charles Blundell:
Memory-based Parameter Adaptation. ICLR (Poster) 2018 - [c32]Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu:
Mix & Match Agent Curricula for Reinforcement Learning. ICML 2018: 1095-1103 - [c31]Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick:
Been There, Done That: Meta-Learning with Episodic Recall. ICML 2018: 4351-4360 - [c30]Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell:
Progress & Compress: A scalable framework for continual learning. ICML 2018: 4535-4544 - [c29]Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap:
Relational recurrent neural networks. NeurIPS 2018: 7310-7321 - [i41]Antonio Polino, Razvan Pascanu, Dan Alistarh:
Model compression via distillation and quantization. CoRR abs/1802.05668 (2018) - [i40]Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae, Alexander Pritzel, Adrià Puigdomènech Badia, Benigno Uria, Oriol Vinyals, Demis Hassabis, Razvan Pascanu, Charles Blundell:
Memory-based Parameter Adaptation. CoRR abs/1802.10542 (2018) - [i39]Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter W. Battaglia:
Learning Deep Generative Models of Graphs. CoRR abs/1803.03324 (2018) - [i38]Yao Lu, Mehrtash Harandi, Richard I. Hartley, Razvan Pascanu:
Block Mean Approximation for Efficient Second Order Optimization. CoRR abs/1804.05484 (2018) - [i37]Thomas S. Stepleton, Razvan Pascanu, Will Dabney, Siddhant M. Jayakumar, Hubert Soyer, Rémi Munos:
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery. CoRR abs/1805.04955 (2018) - [i36]Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell:
Progress & Compress: A scalable framework for continual learning. CoRR abs/1805.06370 (2018) - [i35]Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick:
Been There, Done That: Meta-Learning with Episodic Recall. CoRR abs/1805.09692 (2018) - [i34]Çaglar Gülçehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter W. Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas:
Hyperbolic Attention Networks. CoRR abs/1805.09786 (2018) - [i33]Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew M. Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu:
Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018) - [i32]Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu:
Mix&Match - Agent Curricula for Reinforcement Learning. CoRR abs/1806.01780 (2018) - [i31]Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap:
Relational recurrent neural networks. CoRR abs/1806.01822 (2018) - [i30]