Razvan Pascanu

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 168

Top Venues

Publications

Simin Fan, Razvan Pascanu, Martin Jaggi
Deep Grokking: Would Deep Neural Networks Generalize Better? CoRR (2024)
Soham De, Samuel L. Smith, Anushan Fernando, Aleksandar Botev, George-Cristian Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, Arnaud Doucet, David Budden, Yee Whye Teh, Razvan Pascanu, Nando de Freitas, Caglar Gulcehre
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. CoRR (2024)
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. Trans. Mach. Learn. Res. 2024 (2024)
Skander Moalla, Andrea Miele, Razvan Pascanu, Caglar Gulcehre
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO. CoRR (2024)
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. Trans. Mach. Learn. Res. 2024 (2024)
Xiuying Wei, Skander Moalla, Razvan Pascanu, Caglar Gulcehre
Investigating Low-Rank Training in Transformer Language Models: Efficiency and Scaling Analysis. CoRR (2024)
Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu
Attention as a Hypernetwork. CoRR (2024)
Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz GUStavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR (2024)
Wilfried Bounsi, Borja Ibarz, Andrew Dudzik, Jessica B. Hamrick, Larisa Markeeva, Alex Vitvitskyi, Razvan Pascanu, Petar Velickovic
Transformers meet Neural Algorithmic Reasoners. CoRR (2024)
Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. CoRR (2024)
Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento
When can transformers compositionally generalize in-context? CoRR (2024)
Maciej Pióro, Maciej Wolczyk, Razvan Pascanu, Johannes von Oswald, João Sacramento
State Soup: In-Context Skill Learning, Retrieval and Mixing. CoRR (2024)
Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João G. M. Araújo, Alex Vitvitskyi, Razvan Pascanu, Petar Velickovic
Transformers need glasses! Information over-squashing in language tasks. CoRR (2024)
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
Kalman Filter for Online Classification of Non-Stationary Data. ICLR (2024)
Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR (2024)
Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger
Discovering modular solutions that generalize compositionally. ICLR (2024)
Ioana Bica, Anastasija Ilic, Matthias Bauer, Goker Erdogan, Matko Bosnjak, Christos Kaplanis, Alexey A. Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, Jovana Mitrovic
Improving fine-grained understanding in image-text pre-training. CoRR (2024)
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
Normalization and effective learning rates in reinforcement learning. CoRR (2024)
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
Disentangling the Causes of Plasticity Loss in Neural Networks. CoRR (2024)
Maciej Wolczyk, Bartlomiej Cupial, Mateusz Ostaszewski, Michal Bortkiewicz, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem. CoRR (2024)
Xiuying Wei, Skander Moalla, Razvan Pascanu, Caglar Gulcehre
Building on Efficient Foundations: Effectively Training LLMs with Structured Feedforward Layers. CoRR (2024)
Eli Verwimp, 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 (2023)
Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De
Resurrecting Recurrent Neural Networks for Long Sequences. ICML (2023)
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)
Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani, Razvan Pascanu, Piotr Milos, Tomasz Trzcinski
The Tunnel Effect: Building Data Representations in Deep Neural Networks. NeurIPS (2023)
Andrew Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic
Asynchronous Algorithmic Alignment with Cocycles. CoRR (2023)
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
Understanding plasticity in neural networks. CoRR (2023)
Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic
Latent Space Representations of Neural Algorithmic Reasoners. LoG (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. CoRR (2023)
Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic
Latent Space Representations of Neural Algorithmic Reasoners. CoRR (2023)
Thomas Schmied, Markus Hofmarcher, Fabian Paischer, Razvan Pascanu, Sepp Hochreiter
Learning to Modulate pre-trained Models in RL. CoRR (2023)
Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De
Resurrecting Recurrent Neural Networks for Long Sequences. CoRR (2023)
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 (2023)
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 (2023)
Adam Fisch, Amal Rannen-Triki, Razvan Pascanu, Jörg Bornschein, Angeliki Lazaridou, Elena Gribovskaya, Marc'Aurelio Ranzato
Towards Robust and Efficient Continual Language Learning. CoRR (2023)
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)
Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wolczyk, Alexandra Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger
Discovering modular solutions that generalize compositionally. CoRR (2023)
Antonio Orvieto, Soham De, Çaglar Gülçehre, Razvan Pascanu, Samuel L. Smith
On the Universality of Linear Recurrences Followed by Nonlinear Projections. CoRR (2023)
Andrew Joseph Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic
Asynchronous Algorithmic Alignment With Cocycles. LoG (2023)
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 (2023)
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 (2023)
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 (2023)
Thomas Schmied, Markus Hofmarcher, Fabian Paischer, Razvan Pascanu, Sepp Hochreiter
Learning to Modulate pre-trained Models in RL. NeurIPS (2023)
Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani, Razvan Pascanu, Piotr Milos, Tomasz Trzcinski
The Tunnel Effect: Building Data Representations in Deep Neural Networks. CoRR (2023)
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle, Razvan Pascanu, Will Dabney, André Barreto
Deep Reinforcement Learning with Plasticity Injection. NeurIPS (2023)
Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu
Continually learning representations at scale. CoLLAs (2023)
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney
Understanding Plasticity in Neural Networks. ICML (2023)
Andrei Alex Rusu, Sebastian Flennerhag, Dushyant Rao, Razvan Pascanu, Raia Hadsell
Probing Transfer in Deep Reinforcement Learning without Task Engineering. CoLLAs (2022)
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 (2022)
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 (2022)
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)
Andrei A. Rusu, Sebastian Flennerhag, Dushyant Rao, Razvan Pascanu, Raia Hadsell
Probing Transfer in Deep Reinforcement Learning without Task Engineering. CoRR (2022)
Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell
The CLRS Algorithmic Reasoning Benchmark. CoRR (2022)
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 (2022)
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu
When Does Re-initialization Work? CoRR (2022)
Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell
Reasoning-Modulated Representations. LoG (2022)
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 (2022)
Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell
The CLRS Algorithmic Reasoning Benchmark. ICML (2022)
Florina-Cristina Calnegru, John Shawe-Taylor, Iasonas Kokkinos, Razvan Pascanu
Correlation Based Semantic Transfer with Application to Domain Adaptation. ICONIP (1) (2022)
Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi
Test Sample Accuracy Scales with Training Sample Density in Neural Networks. CoLLAs (2022)
Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos
Disentangling Transfer in Continual Reinforcement Learning. CoRR (2022)
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)
Seyed-Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Timothy Nguyen, Razvan Pascanu, Dilan Görür, Mehrdad Farajtabar
Architecture Matters in Continual Learning. CoRR (2022)
Seyed-Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Görür, Mehrdad Farajtabar
Wide Neural Networks Forget Less Catastrophically. ICML (2022)
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 (2022)
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu
When Does Re-initialization Work? ICBINB (2022)
Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos
Disentangling Transfer in Continual Reinforcement Learning. NeurIPS (2022)
Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen Triki, Razvan Pascanu
On the Role of Optimization in Double Descent: A Least Squares Study. CoRR (2021)
Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio
Predicting Unreliable Predictions by Shattering a Neural Network. CoRR (2021)
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
Powerpropagation: A sparsity inducing weight reparameterisation. NeurIPS (2021)
Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell
Reasoning-Modulated Representations. CoRR (2021)
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Dilan Görür, Razvan Pascanu, Hassan Ghasemzadeh
Linear Mode Connectivity in Multitask and Continual Learning. ICLR (2021)
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)
Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective. CoRR (2021)
Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, Erich Elsen
Top-KAST: Top-K Always Sparse Training. CoRR (2021)
Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, Samuel L. Smith
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error. CoRR (2021)
Tudor Berariu, Wojciech Czarnecki, Soham De, Jörg Bornschein, Samuel L. Smith, Razvan Pascanu, Claudia Clopath
A study on the plasticity of neural networks. CoRR (2021)
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 (2021)
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 (2021)
Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos
Continual World: A Robotic Benchmark For Continual Reinforcement Learning. NeurIPS (2021)
Seyed-Iman Mirzadeh, Arslan Chaudhry, Huiyi Hu, Razvan Pascanu, Dilan Görür, Mehrdad Farajtabar
Wide Neural Networks Forget Less Catastrophically. CoRR (2021)
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)
Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos
Continual World: A Robotic Benchmark For Continual Reinforcement Learning. CoRR (2021)
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
Powerpropagation: A sparsity inducing weight reparameterisation. CoRR (2021)
Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective. ICML (2021)
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh
Understanding the Role of Training Regimes in Continual Learning. CoRR (2020)
Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, Erich Elsen
Top-KAST: Top-K Always Sparse Training. NeurIPS (2020)
Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, Raia Hadsell
Meta-Learning with Warped Gradient Descent. ICLR (2020)
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Dilan Görür, Razvan Pascanu, Hassan Ghasemzadeh
Linear Mode Connectivity in Multitask and Continual Learning. CoRR (2020)
Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
Pointer Graph Networks. CoRR (2020)
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 (2020)
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 (2020)
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 (2020)
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)
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)
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 Botvinick, Nicolas Heess, Raia Hadsell
Stabilizing Transformers for Reinforcement Learning. ICML (2020)
Albert Gu, Çaglar Gülçehre, Thomas Paine, Matt Hoffman, Razvan Pascanu
Improving the Gating Mechanism of Recurrent Neural Networks. ICML (2020)
Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
Pointer Graph Networks. NeurIPS (2020)
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh
Understanding the Role of Training Regimes in Continual Learning. NeurIPS (2020)
Dushyant Rao, Francesco Visin, Andrei A. Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell
Continual Unsupervised Representation Learning. NeurIPS (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Razvan Pascanu