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2020 – today
- 2021
[c47]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:
On Robustness and Transferability of Convolutional Neural Networks. CVPR 2021: 16458-16468- [c46]Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. ICLR 2021 - [c45]Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby:
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR 2021 - [c44]Joan Puigcerver, Carlos Riquelme Ruiz, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby:
Scalable Transfer Learning with Expert Models. ICLR 2021 - [c43]Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle:
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. NeurIPS Datasets and Benchmarks 2021 - [i43]Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle:
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. CoRR abs/2104.02638 (2021) - [i42]Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai:
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size. CoRR abs/2104.04191 (2021) - 2020
- [j8]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. 21: 209:1-209:62 (2020) - [j7]Sjoerd van Steenkiste
, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly:
Investigating object compositionality in Generative Adversarial Networks. Neural Networks 130: 309-325 (2020) - [c42]Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly:
Google Research Football: A Novel Reinforcement Learning Environment. AAAI 2020: 4501-4510 - [c41]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI 2020: 13681-13684 - [c40]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Precision-Recall Curves Using Information Divergence Frontiers. AISTATS 2020: 2550-2559 - [c39]Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic:
Self-Supervised Learning of Video-Induced Visual Invariances. CVPR 2020: 13803-13812 - [c38]Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby:
Big Transfer (BiT): General Visual Representation Learning. ECCV (5) 2020: 491-507 - [c37]Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic:
On Mutual Information Maximization for Representation Learning. ICLR 2020 - [c36]Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers:
What Do Neural Networks Learn When Trained With Random Labels? NeurIPS 2020 - [i41]Nicolas Brosse, Carlos Riquelme, Alice Martin, Sylvain Gelly, Eric Moulines:
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation. CoRR abs/2001.08049 (2020) - [i40]Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin:
Predicting Neural Network Accuracy from Weights. CoRR abs/2002.11448 (2020) - [i39]Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. CoRR abs/2006.05990 (2020) - [i38]Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers:
What Do Neural Networks Learn When Trained With Random Labels? CoRR abs/2006.10455 (2020) - [i37]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:
On Robustness and Transferability of Convolutional Neural Networks. CoRR abs/2007.08558 (2020) - [i36]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR abs/2007.14184 (2020) - [i35]Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cédric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, Neil Houlsby:
Scalable Transfer Learning with Expert Models. CoRR abs/2009.13239 (2020) - [i34]Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby:
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR abs/2010.11929 (2020) - [i33]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR abs/2010.14766 (2020)
2010 – 2019
- 2019
- [c35]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? COLT 2019: 1500-1518 - [c34]Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly:
On Self Modulation for Generative Adversarial Networks. ICLR (Poster) 2019 - [c33]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR 2019 - [c32]Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphaël Marinier, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly:
Episodic Curiosity through Reachability. ICLR (Poster) 2019 - [c31]Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly:
FVD: A new Metric for Video Generation. DGS@ICLR 2019 - [c30]Octavian Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn:
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. ICML 2019: 2073-2082 - [c29]Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly:
Parameter-Efficient Transfer Learning for NLP. ICML 2019: 2790-2799 - [c28]Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly:
A Large-Scale Study on Regularization and Normalization in GANs. ICML 2019: 3581-3590 - [c27]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML 2019: 4114-4124 - [c26]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. ICML 2019: 4183-4192 - [c25]Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu:
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. NeurIPS 2019: 11872-11882 - [i32]Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly:
Parameter-Efficient Transfer Learning for NLP. CoRR abs/1902.00751 (2019) - [i31]Octavian-Eugen Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn:
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. CoRR abs/1902.08077 (2019) - [i30]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. CoRR abs/1903.02271 (2019) - [i29]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Evaluating Generative Models Using Divergence Frontiers. CoRR abs/1905.10768 (2019) - [i28]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? CoRR abs/1905.11866 (2019) - [i27]Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy A. Mann, André Barreto, Sylvain Gelly, Gergely Neu:
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. CoRR abs/1906.07987 (2019) - [i26]Lucas Beyer, Damien Vincent, Olivier Teboul, Sylvain Gelly, Matthieu Geist, Olivier Pietquin:
MULEX: Disentangling Exploitation from Exploration in Deep RL. CoRR abs/1907.00868 (2019) - [i25]Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly:
Google Research Football: A Novel Reinforcement Learning Environment. CoRR abs/1907.11180 (2019) - [i24]Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic:
On Mutual Information Maximization for Representation Learning. CoRR abs/1907.13625 (2019) - [i23]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:
The Visual Task Adaptation Benchmark. CoRR abs/1910.04867 (2019) - [i22]Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic:
Semantic Bottleneck Scene Generation. CoRR abs/1911.11357 (2019) - [i21]Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic:
Self-Supervised Learning of Video-Induced Visual Invariances. CoRR abs/1912.02783 (2019) - [i20]Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby:
Large Scale Learning of General Visual Representations for Transfer. CoRR abs/1912.11370 (2019) - 2018
- [c24]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. ICLR (Workshop) 2018 - [c23]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. ICLR 2018 - [c22]Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet:
Are GANs Created Equal? A Large-Scale Study. NeurIPS 2018: 698-707 - [c21]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. NeurIPS 2018: 5234-5243 - [i19]Hartmut Maennel, Olivier Bousquet, Sylvain Gelly:
Gradient Descent Quantizes ReLU Network Features. CoRR abs/1803.08367 (2018) - [i18]Sylvain Gelly, Karol Kurach, Marcin Michalski, Xiaohua Zhai:
MemGEN: Memory is All You Need. CoRR abs/1803.11203 (2018) - [i17]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. CoRR abs/1804.11130 (2018) - [i16]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. CoRR abs/1806.00035 (2018) - [i15]Stanislau Semeniuta, Aliaksei Severyn, Sylvain Gelly:
On Accurate Evaluation of GANs for Language Generation. CoRR abs/1806.04936 (2018) - [i14]Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto:
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem. CoRR abs/1807.03064 (2018) - [i13]Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly:
The GAN Landscape: Losses, Architectures, Regularization, and Normalization. CoRR abs/1807.04720 (2018) - [i12]Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly:
On Self Modulation for Generative Adversarial Networks. CoRR abs/1810.01365 (2018) - [i11]Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly:
Episodic Curiosity through Reachability. CoRR abs/1810.02274 (2018) - [i10]Sjoerd van Steenkiste, Karol Kurach, Sylvain Gelly:
A Case for Object Compositionality in Deep Generative Models of Images. CoRR abs/1810.10340 (2018) - [i9]Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR abs/1811.12359 (2018) - [i8]Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly:
Towards Accurate Generative Models of Video: A New Metric & Challenges. CoRR abs/1812.01717 (2018) - 2017
- [c20]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. NIPS 2017: 5424-5433 - [i7]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. CoRR abs/1701.02386 (2017) - [i6]Karol Kurach, Sylvain Gelly, Michal Jastrzebski, Philip Häusser, Olivier Teytaud, Damien Vincent, Olivier Bousquet:
Better Text Understanding Through Image-To-Text Transfer. CoRR abs/1705.08386 (2017) - [i5]Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Damien Vincent:
Toward Optimal Run Racing: Application to Deep Learning Calibration. CoRR abs/1706.03199 (2017) - [i4]Olivier Bousquet, Sylvain Gelly, Karol Kurach, Olivier Teytaud, Damien Vincent:
Critical Hyper-Parameters: No Random, No Cry. CoRR abs/1706.03200 (2017) - [i3]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. CoRR abs/1711.01558 (2017) - [i2]Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet:
Are GANs Created Equal? A Large-Scale Study. CoRR abs/1711.10337 (2017) - 2012
- [j6]Sylvain Gelly, Levente Kocsis, Marc Schoenauer, Michèle Sebag, David Silver, Csaba Szepesvári, Olivier Teytaud:
The grand challenge of computer Go: Monte Carlo tree search and extensions. Commun. ACM 55(3): 106-113 (2012) - 2011
- [j5]Sylvain Gelly, David Silver:
Monte-Carlo tree search and rapid action value estimation in computer Go. Artif. Intell. 175(11): 1856-1875 (2011)
2000 – 2009
- 2009
- [j4]Guillaume Chaslot, L. Chatriot, Christophe Fiter, Sylvain Gelly, Jean-Baptiste Hoock, Julien Perez, Arpad Rimmel, Olivier Teytaud:
Combiner connaissances expertes, hors-ligne, transientes et en ligne pour l'exploration Monte-Carlo. Apprentissage et MC. Rev. d'Intelligence Artif. 23(2-3): 203-220 (2009) - [c19]Nur Merve Amil, Nicolas Bredèche, Christian Gagné, Sylvain Gelly, Marc Schoenauer, Olivier Teytaud:
A Statistical Learning Perspective of Genetic Programming. EuroGP 2009: 327-338 - 2008
- [c18]Sylvain Gelly, David Silver:
Achieving Master Level Play in 9 x 9 Computer Go. AAAI 2008: 1537-1540 - [c17]Sylvain Gelly, Jean-Baptiste Hoock, Arpad Rimmel, Olivier Teytaud, Y. Kalemkarian:
The Parallelization of Monte-Carlo Planning - Parallelization of MC-Planning. ICINCO-ICSO 2008: 244-249 - 2007
- [j3]Sylvain Gelly, Sylvie Ruette, Olivier Teytaud:
Comparison-Based Algorithms Are Robust and Randomized Algorithms Are Anytime. Evol. Comput. 15(4): 411-434 (2007) - [c16]Yizao Wang, Sylvain Gelly:
Modifications of UCT and sequence-like simulations for Monte-Carlo Go. CIG 2007: 175-182 - [c15]Olivier Teytaud, Sylvain Gelly:
DCMA: yet another derandomization in covariance-matrix-adaptation. GECCO 2007: 955-963 - [c14]Olivier Teytaud, Sylvain Gelly:
Nonlinear programming in approximate dynamic programming - bang-bang solutions, stock-management and unsmooth penalties. ICINCO-ICSO 2007: 47-54 - [c13]Olivier Teytaud, Sylvain Gelly, Jérémie Mary:
Active learning in regression, with application to stochastic dynamic programming. ICINCO-ICSO 2007: 198-205 - [c12]Sylvain Gelly, David Silver:
Combining online and offline knowledge in UCT. ICML 2007: 273-280 - 2006
- [j2]Sylvain Gelly, Olivier Teytaud:
Bayesian Networks: a Non-Frequentist Approach for Parametrization, and a more Accurate Structural Complexity Measure Bayesian Networks Learning. Rev. d'Intelligence Artif. 20(6): 717-755 (2006) - [j1]Sylvain Gelly, Olivier Teytaud, Nicolas Bredèche, Marc Schoenauer:
Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint. Rev. d'Intelligence Artif. 20(6): 805-827 (2006) - [c11]Sylvain Gelly, Olivier Teytaud, Christian Gagné:
Resource-Aware Parameterizations of EDA. IEEE Congress on Evolutionary Computation 2006: 2506-2512 - [c10]Sylvain Gelly, Jérémie Mary, Olivier Teytaud:
Learning for stochastic dynamic programming. ESANN 2006: 191-196 - [c9]Olivier Teytaud, Sylvain Gelly:
General Lower Bounds for Evolutionary Algorithms. PPSN 2006: 21-31 - [c8]Olivier Teytaud, Sylvain Gelly, Jérémie Mary:
On the Ultimate Convergence Rates for Isotropic Algorithms and the Best Choices Among Various Forms of Isotropy. PPSN 2006: 32-41 - 2005
- [c7]Sylvain Gelly, Nicolas Bredèche, Michèle Sebag:
HMM hiérarchiques et factorisés: mécanisme d'inférence et apprentissage à partir de peu de données. CAP 2005: 143-144 - [c6]Sylvain Gelly, Olivier Teytaud:
Statistical asymptotic and non-asymptotic consistency of bayesian networks: convergence to the right structure and consistent probability estimates. CAP 2005: 147-162 - [c5]Sylvain Gelly, Olivier Teytaud, Nicolas Bredèche, Marc Schoenauer:
Apprentissage statistique et programmation génétique: la croissance du code est-elle inévitable? CAP 2005: 163-178 - [c4]Sylvain Gelly, Jérémie Mary, Olivier Teytaud:
Taylor-based pseudo-metrics for random process fitting in dynamic programming: expected loss minimization and risk management. CAP 2005: 183-184 - [c3]Sylvain Gelly, Michèle Sebag, Nicolas Bredèche:
Inférence dans les HMM hiérarchiques et factorisés : changement de représentation vers le formalisme des Réseaux Bayésiens. EGC (Ateliers) 2005: 57-60 - [c2]Sylvain Gelly, Olivier Teytaud, Nicolas Bredèche, Marc Schoenauer:
A statistical learning theory approach of bloat. GECCO 2005: 1783-1784 - [c1]Sylvain Gelly, Nicolas Bredèche, Michèle Sebag:
From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm. SARA 2005: 107-120 - [i1]Yann Semet, Sylvain Gelly, Marc Schoenauer, Michèle Sebag:
Artificial Agents and Speculative Bubbles. CoRR abs/cs/0511093 (2005)
Coauthor Index
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