Rémi Tachet des Combes

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 9 papers; trusts 304 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 140 citations into it; #16,909 of 35,345 by trust here, #8,439 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #9,450 of 21,575 by trust here, #11,498 by citations
• Reinforcement Learning — 60 citations into it; #1,449 of 2,242 by trust here, #1,501 by citations
• Generative Models — 37 citations into it; #885 of 1,956 by trust here, #1,271 by citations
• Optimization & Training — 36 citations into it; #2,294 of 2,601 by trust here, #1,105 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Management Science and Operations Research — 17 citations into it
• Safety, Robustness & Interpretability — 13 citations into it; #1,524 of 2,251 by trust here, #2,041 by citations

Whom Rémi Tachet des Combes trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Diederik P. Kingma
6. Karen Simonyan
7. Kaiming He
8. Ilya Sutskever
9. Richard S. Sutton
10. Andrew Zisserman

Who trusts Rémi Tachet des Combes overall

1. Bo Zhao
2. Valentin De Bortoli
3. Aviral Kumar
4. Arash Vahdat
5. Chenlin Meng
6. Thiago D. Simão
7. Jiaming Song
8. Daehee Kim
9. Qinsheng Zhang

Papers from Rémi Tachet des Combes's perspective

Ordered by how strongly the researchers Rémi Tachet des Combes 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Adversarial score matching and improved sampling for image generation (2020) — 138 citations
• An Empirical Study of Example Forgetting during Deep Neural Network Learning (2018) — 1,017 citations
• Counting to Explore and Generalize in Text-based Games (2018) — 76 citations
• On the Learning Dynamics of Deep Neural Networks (2018) — 44 citations
• Safe Policy Improvement with an Estimated Baseline Policy (2019) — 26 citations
• Safe Policy Improvement with Soft Baseline Bootstrapping (2019) — 37 citations
• Measuring the Carbon Intensity of AI in Cloud Instances (2022) — 311 citations
• Multi-batch Reinforcement Learning (2019) — 3 citations
• On Learning Invariant Representations for Domain Adaptation (2019) — 558 citations