Tristan Deleu

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 8 papers; trusts 117 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 143 citations into it; #30,400 of 35,345 by trust here, #17,412 by citations
• Reinforcement Learning — 77 citations into it
• Probabilistic & Bayesian Methods — 66 citations into it
• Causal Inference & Discovery — 39 citations into it; #330 of 364 by trust here, #266 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Statistics and Probability — 24 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #2,309 of 3,145 by trust here, #1,754 by citations
• Generative Models — 16 citations into it

Whom Tristan Deleu trusts overall

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

1. Yoshua Bengio
2. Richard S. Sutton
3. Ronald J. Williams
4. J. Urgen Schmidhuber
5. Chelsea Finn
6. Geoffrey E. Hinton
7. Jan Peters
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Tristan Deleu overall

1. Yoshua Bengio
2. Anirudh Goyal
3. Bernhard Schölkopf
4. Gary F. Marcus
5. Rémi Le Priol
6. Nan Rosemary Ke
7. Han-Jia Ye
8. Da-Wei Zhou
9. Sébastien Lachapelle
10. Khurram Javed

Papers from Tristan Deleu's perspective

Ordered by how strongly the researchers Tristan Deleu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Learning Powerful Policies by Using Consistent Dynamics Model (2019) — 5 citations
• Gradient-Based Neural DAG Learning (2019) — 359 citations
• Bayesian Structure Learning with Generative Flow Networks (2022) — 202 citations
• Gymnasium: A Standard Interface for Reinforcement Learning Environments (2025) — 797 citations
• GFlowNet Foundations (2021) — 363 citations
• COVI White Paper (2020) — 30 citations
• A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms (2019) — 379 citations
• The effects of negative adaptation in Model-Agnostic Meta-Learning (2018) — 23 citations