Sylvain Gelly
24 papers · 2017–2021 · 9 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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π£ Hot Topic Early Bird π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (43) π€ Dynamic Duo (11)
π§
Keyword Pioneer
π
Triple Crown
π€
Dynamic Duo
(11)
β‘
Prolific Year Β· Productive
π₯
Unstoppable Β· Bronze
β
The Questioner
(4)
π
Century Club Β· Bronze
π
Conference Pioneer
π
Trend Setter
ποΈ
Keyword Collector
(92)
Top co-authors
Research topics
Deep Learning
(17)
Machine Learning
(17)
Learning Types Β· Machine Learning
(10)
Models
(8)
Artificial Intelligence
(6)
Core Methods
(6)
Architectures
(5)
Learning Types Β· Deep Learning
(5)
Reinforcement Learning
(4)
Core AI
(4)
Methods
(4)
Optimization & Theory
(4)
Techniques
(4)
Computer Vision
(2)
Application Areas
(2)
Keywords
transfer learning
(6)
generative adversarial network
(6)
representation learning
(5)
reinforcement learning
(3)
self-supervised learning
(3)
inductive bia
(3)
generative model
(3)
unsupervised learning
(2)
image generation
(2)
evaluation metric
(2)
precision and recall
(2)
disentangled representation
(2)
semi-supervised learning
(2)
domain generalization
(1)
few-shot learning
(1)
metric learning
(1)
policy evaluation
(1)
hyperparameter optimization
(1)
vision transformer
(1)
image classification
(1)
Papers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
ICLR 2021
On Robustness and Transferability of Convolutional Neural Networks
CVPR 2021
Scalable Transfer Learning with Expert Models
ICLR 2021
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
ICLR 2021
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
JMLR 2020
Big Transfer (BiT): General Visual Representation Learning
ECCV 2020
Google Research Football: A Novel Reinforcement Learning Environment
AAAI 2020
On Mutual Information Maximization for Representation Learning
ICLR 2020
Precision-Recall Curves Using Information Divergence Frontiers
AISTATS 2020
Self-Supervised Learning of Video-Induced Visual Invariances
CVPR 2020
What Do Neural Networks Learn When Trained With Random Labels?
NeurIPS 2020
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
NeurIPS 2019
A Large-Scale Study on Regularization and Normalization in GANs
ICML 2019
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
ICML 2019
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
ICML 2019
Episodic Curiosity through Reachability
ICLR 2019
High-Fidelity Image Generation With Fewer Labels
ICML 2019
On Self Modulation for Generative Adversarial Networks
ICLR 2019
Parameter-Efficient Transfer Learning for NLP
ICML 2019
When can unlabeled data improve the learning rate?
COLT 2019
Are GANs Created Equal? A Large-Scale Study
NeurIPS 2018
Assessing Generative Models via Precision and Recall
NeurIPS 2018
Wasserstein Auto-Encoders
ICLR 2018
AdaGAN: Boosting Generative Models
NeurIPS 2017