Michèle Sebag

Artificial Intelligence · with Guyon · 2010–2019

A researcher in the Science corpus: 15 papers; trusts 313 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 214 citations into it; #8,587 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Optimization & Training — 66 citations into it; #920 of 2,601 by trust here, #1,083 by citations
• Causal Inference & Discovery — 42 citations into it; #358 of 364 by trust here, #181 by citations
• Statistics and Probability — 41 citations into it
• Reinforcement Learning — 39 citations into it; #650 of 2,242 by trust here, #1,075 by citations
• Representation & Self-Supervised Learning — 38 citations into it
• Computational Theory and Mathematics — 36 citations into it

Whom Michèle Sebag trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Richard S. Sutton
7. Jan Peters
8. Andrew Y. Ng
9. Ilya Sutskever
10. Bernhard Schölkopf

Who trusts Michèle Sebag overall

1. Christian Wirth
2. Marc-André Zöller
3. Martin Wistuba
4. Riad Akrour
5. Frank Hutter
6. Róbert Istvan Busa-Fekete
7. Jan Leike
8. Matthias Feurer
9. Ellen R. Novoseller
10. Paul Christiano

Papers from Michèle Sebag's perspective

Ordered by how strongly the researchers Michèle Sebag trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The grand challenge of computer Go (2012) — 240 citations
• APRIL: Active Preference-learning based Reinforcement Learning (2012) — 126 citations
• Causal Generative Neural Networks (2017) — 69 citations
• Graph inference datasets. Replication Data for: "Learning Functional Causal Models with Generative Neural Networks" (2017) — 35 citations
• Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018) — 121 citations
• Multi-Domain Adversarial Learning (2019) — 65 citations
• Analysis of the AutoML Challenge Series 2015-2018 (2019) — 189 citations
• SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning (2018) — 47 citations
• Alors: An algorithm recommender system (2017) — 87 citations
• Stochastic Gradient Descent: Going As Fast As Possible But Not Faster (2017) — 14 citations
• A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention (2016) — 84 citations
• Programming by Feedback (2014) — 72 citations
• Collaborative hyperparameter tuning (2013) — 384 citations
• Preference-Based Policy Learning (2011)
• Feature Selection as a One-Player Game (2010) — 118 citations