Sébastien Lachapelle

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 73 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #24,695 of 35,345 by trust here, #20,377 by citations
• Causal Inference & Discovery — 63 citations into it; #281 of 364 by trust here, #212 by citations
• Probabilistic & Bayesian Methods — 42 citations into it
• Statistics and Probability — 36 citations into it
• Representation & Self-Supervised Learning — 32 citations into it; #2,306 of 3,145 by trust here, #1,741 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Management Science and Operations Research — 22 citations into it
• Optimization & Training — 22 citations into it

Whom Sébastien Lachapelle trusts overall

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

1. Jan Peters
2. Yoshua Bengio
3. Aapo Hyvärinen
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Peter Buhlmann
7. Bernhard Schölkopf
8. Yann LeCun
9. Xavier Glorot
10. Alex Krizhevsky

Who trusts Sébastien Lachapelle overall

1. Gonçalo Faria
2. Rémi Le Priol
3. Chao Shang
4. Ignavier Ng
5. Philippe Brouillard
6. Xun Zheng
7. Yashas Annadani
8. Yoshua Bengio
9. André F. T. Martins
10. Alexandre Lacoste

Papers from Sébastien Lachapelle's perspective

Ordered by how strongly the researchers Sébastien Lachapelle 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Gradient-Based Neural DAG Learning (2019) — 359 citations
• Differentiable Causal Discovery from Interventional Data (2020) — 282 citations
• Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA (2021) — 190 citations
• A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms (2019) — 379 citations
• Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information (2018) — 52 citations