Sylvain Gelly - RankMe

98 papers A* 20A 5B 3Journal 51Unranked 19

Year	Rank	Type	Title / Venue / Authors
2021	—	`conf`	A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. NeurIPS Datasets and Benchmarks Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle
2021	A*	`conf`	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
2021	J	`jnl`	Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. CoRR Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle
2021	A*	`conf`	On Robustness and Transferability of Convolutional Neural Networks. CVPR Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
2021	J	`jnl`	SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size. CoRR Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai
2021	A*	`conf`	Scalable Transfer Learning with Expert Models. ICLR Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
2021	A*	`conf`	What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. ICLR Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
2020	A*	`conf`	A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
2020	—	`conf`	Big Transfer (BiT): General Visual Representation Learning. ECCV (5) Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby
2020	A*	`conf`	Google Research Football: A Novel Reinforcement Learning Environment. AAAI Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly
2020	J	`jnl`	Investigating object compositionality in Generative Adversarial Networks. Neural Networks Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly
2020	J	`jnl`	On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation. CoRR Nicolas Brosse, Carlos Riquelme, Alice Martin, Sylvain Gelly, Eric Moulines
2020	A*	`conf`	On Mutual Information Maximization for Representation Learning. ICLR Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic
2020	J	`jnl`	On Robustness and Transferability of Convolutional Neural Networks. CoRR Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
2020	A	`conf`	Precision-Recall Curves Using Information Divergence Frontiers. AISTATS Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
2020	J	`jnl`	Predicting Neural Network Accuracy from Weights. CoRR Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin
2020	J	`jnl`	Scalable Transfer Learning with Expert Models. CoRR Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby
2020	A*	`conf`	Self-Supervised Learning of Video-Induced Visual Invariances. CVPR Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic
2020	A*	`conf`	What Do Neural Networks Learn When Trained With Random Labels? NeurIPS Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
2020	J	`jnl`	What Do Neural Networks Learn When Trained With Random Labels? CoRR Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
2020	J	`jnl`	What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. CoRR Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
2019	A*	`conf`	A Large-Scale Study on Regularization and Normalization in GANs. ICML Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly
2019	A*	`conf`	Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. NeurIPS Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu
2019	J	`jnl`	Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. CoRR Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy A. Mann, André Barreto, Sylvain Gelly, Gergely Neu
2019	A*	`conf`	Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. ICML Octavian Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn
2019	J	`jnl`	Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. CoRR Octavian-Eugen Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn
2019	—	`conf`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2019	A*	`conf`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2019	—	`conf`	Episodic Curiosity through Reachability. ICLR (Poster) Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphaël Marinier, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly
2019	J	`jnl`	Evaluating Generative Models Using Divergence Frontiers. CoRR Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
2019	—	`conf`	FVD: A new Metric for Video Generation. DGS@ICLR Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly
2019	J	`jnl`	Google Research Football: A Novel Reinforcement Learning Environment. CoRR Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly
2019	A*	`conf`	High-Fidelity Image Generation With Fewer Labels. ICML Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
2019	J	`jnl`	High-Fidelity Image Generation With Fewer Labels. CoRR Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
2019	J	`jnl`	Large Scale Learning of General Visual Representations for Transfer. CoRR Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby
2019	J	`jnl`	MULEX: Disentangling Exploitation from Exploration in Deep RL. CoRR Lucas Beyer, Damien Vincent, Olivier Teboul, Sylvain Gelly, Matthieu Geist, Olivier Pietquin
2019	J	`jnl`	On Mutual Information Maximization for Representation Learning. CoRR Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic
2019	—	`conf`	On Self Modulation for Generative Adversarial Networks. ICLR (Poster) Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly
2019	A*	`conf`	Parameter-Efficient Transfer Learning for NLP. ICML Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly
2019	J	`jnl`	Parameter-Efficient Transfer Learning for NLP. CoRR Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly
2019	J	`jnl`	Self-Supervised Learning of Video-Induced Visual Invariances. CoRR Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic
2019	J	`jnl`	Semantic Bottleneck Scene Generation. CoRR Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic
2019	J	`jnl`	The Visual Task Adaptation Benchmark. CoRR Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby
2019	A*	`conf`	When can unlabeled data improve the learning rate? COLT Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
2019	J	`jnl`	When can unlabeled data improve the learning rate? CoRR Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
2018	J	`jnl`	A Case for Object Compositionality in Deep Generative Models of Images. CoRR Sjoerd van Steenkiste, Karol Kurach, Sylvain Gelly
2018	A*	`conf`	Are GANs Created Equal? A Large-Scale Study. NeurIPS Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
2018	A*	`conf`	Assessing Generative Models via Precision and Recall. NeurIPS Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
2018	J	`jnl`	Assessing Generative Models via Precision and Recall. CoRR Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
2018	J	`jnl`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2018	—	`conf`	Clustering Meets Implicit Generative Models. ICLR (Workshop) Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
2018	J	`jnl`	Clustering Meets Implicit Generative Models. CoRR Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
2018	J	`jnl`	Episodic Curiosity through Reachability. CoRR Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly
2018	J	`jnl`	Gradient Descent Quantizes ReLU Network Features. CoRR Hartmut Maennel, Olivier Bousquet, Sylvain Gelly
2018	J	`jnl`	MemGEN: Memory is All You Need. CoRR Sylvain Gelly, Karol Kurach, Marcin Michalski, Xiaohua Zhai
2018	J	`jnl`	On Accurate Evaluation of GANs for Language Generation. CoRR Stanislau Semeniuta, Aliaksei Severyn, Sylvain Gelly
2018	J	`jnl`	On Self Modulation for Generative Adversarial Networks. CoRR Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly
2018	J	`jnl`	Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem. CoRR Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto
2018	J	`jnl`	The GAN Landscape: Losses, Architectures, Regularization, and Normalization. CoRR Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly
2018	J	`jnl`	Towards Accurate Generative Models of Video: A New Metric & Challenges. CoRR Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly
2018	—	`conf`	Wasserstein Auto-Encoders. ICLR Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
2017	—	`conf`	AdaGAN: Boosting Generative Models. NIPS Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
2017	J	`jnl`	AdaGAN: Boosting Generative Models. CoRR Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
2017	J	`jnl`	Are GANs Created Equal? A Large-Scale Study. CoRR Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
2017	J	`jnl`	Better Text Understanding Through Image-To-Text Transfer. CoRR Karol Kurach, Sylvain Gelly, Michal Jastrzebski, Philip Häusser, Olivier Teytaud, Damien Vincent, Olivier Bousquet
2017	J	`jnl`	Critical Hyper-Parameters: No Random, No Cry. CoRR Olivier Bousquet, Sylvain Gelly, Karol Kurach, Olivier Teytaud, Damien Vincent
2017	J	`jnl`	Toward Optimal Run Racing: Application to Deep Learning Calibration. CoRR Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Damien Vincent
2017	J	`jnl`	Wasserstein Auto-Encoders. CoRR Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
2012	J	`jnl`	The grand challenge of computer Go: Monte Carlo tree search and extensions. Commun. ACM Sylvain Gelly, Levente Kocsis, Marc Schoenauer, Michèle Sebag, David Silver, Csaba Szepesvári, Olivier Teytaud
2011	J	`jnl`	Monte-Carlo tree search and rapid action value estimation in computer Go. Artif. Intell. Sylvain Gelly, David Silver
2009	B	`conf`	A Statistical Learning Perspective of Genetic Programming. EuroGP Nur Merve Amil, Nicolas Bredèche, Christian Gagné, Sylvain Gelly, Marc Schoenauer, Olivier Teytaud
2009	J	`jnl`	Combiner connaissances expertes, hors-ligne, transientes et en ligne pour l'exploration Monte-Carlo. Apprentissage et MC. Rev. d'Intelligence Artif. Guillaume Chaslot, L. Chatriot, Christophe Fiter, Sylvain Gelly, Jean-Baptiste Hoock, Julien Perez, Arpad Rimmel, Olivier Teytaud
2008	A*	`conf`	Achieving Master Level Play in 9 x 9 Computer Go. AAAI Sylvain Gelly, David Silver
2008	—	`conf`	The Parallelization of Monte-Carlo Planning - Parallelization of MC-Planning. ICINCO-ICSO Sylvain Gelly, Jean-Baptiste Hoock, Arpad Rimmel, Olivier Teytaud, Y. Kalemkarian
2007	—	`conf`	Active learning in regression, with application to stochastic dynamic programming. ICINCO-ICSO Olivier Teytaud, Sylvain Gelly, Jérémie Mary
2007	A*	`conf`	Combining online and offline knowledge in UCT. ICML Sylvain Gelly, David Silver
2007	J	`jnl`	Comparison-Based Algorithms Are Robust and Randomized Algorithms Are Anytime. Evol. Comput. Sylvain Gelly, Sylvie Ruette, Olivier Teytaud
2007	A	`conf`	DCMA: yet another derandomization in covariance-matrix-adaptation. GECCO Olivier Teytaud, Sylvain Gelly
2007	—	`conf`	Modifications of UCT and sequence-like simulations for Monte-Carlo Go. CIG Yizao Wang, Sylvain Gelly
2007	—	`conf`	Nonlinear programming in approximate dynamic programming - bang-bang solutions, stock-management and unsmooth penalties. ICINCO-ICSO Olivier Teytaud, Sylvain Gelly
2006	J	`jnl`	Bayesian Networks: a Non-Frequentist Approach for Parametrization, and a more Accurate Structural Complexity Measure Bayesian Networks Learning. Rev. d'Intelligence Artif. Sylvain Gelly, Olivier Teytaud
2006	A	`conf`	General Lower Bounds for Evolutionary Algorithms. PPSN Olivier Teytaud, Sylvain Gelly
2006	B	`conf`	Learning for stochastic dynamic programming. ESANN Sylvain Gelly, Jérémie Mary, Olivier Teytaud
2006	A	`conf`	On the Ultimate Convergence Rates for Isotropic Algorithms and the Best Choices Among Various Forms of Isotropy. PPSN Olivier Teytaud, Sylvain Gelly, Jérémie Mary
2006	B	`conf`	Resource-Aware Parameterizations of EDA. IEEE Congress on Evolutionary Computation Sylvain Gelly, Olivier Teytaud, Christian Gagné
2006	J	`jnl`	Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint. Rev. d'Intelligence Artif. Sylvain Gelly, Olivier Teytaud, Nicolas Bredèche, Marc Schoenauer
2005	A	`conf`	A statistical learning theory approach of bloat. GECCO Sylvain Gelly, Olivier Teytaud, Nicolas Bredèche, Marc Schoenauer
2005	—	`conf`	Apprentissage statistique et programmation génétique: la croissance du code est-elle inévitable? CAP Sylvain Gelly, Olivier Teytaud, Nicolas Bredèche, Marc Schoenauer
2005	J	`jnl`	Artificial Agents and Speculative Bubbles CoRR Yann Semet, Sylvain Gelly, Marc Schoenauer, Michèle Sebag
2005	—	`conf`	From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm. SARA Sylvain Gelly, Nicolas Bredèche, Michèle Sebag
2005	—	`conf`	HMM hiérarchiques et factorisés: mécanisme d'inférence et apprentissage à partir de peu de données. CAP Sylvain Gelly, Nicolas Bredèche, Michèle Sebag
2005	—	`conf`	Inférence dans les HMM hiérarchiques et factorisés : changement de représentation vers le formalisme des Réseaux Bayésiens. EGC (Ateliers) Sylvain Gelly, Michèle Sebag, Nicolas Bredèche
2005	—	`conf`	Statistical asymptotic and non-asymptotic consistency of bayesian networks: convergence to the right structure and consistent probability estimates. CAP Sylvain Gelly, Olivier Teytaud
2005	—	`conf`	Taylor-based pseudo-metrics for random process fitting in dynamic programming: expected loss minimization and risk management. CAP Sylvain Gelly, Jérémie Mary, Olivier Teytaud