Hugo Caselles-Dupré

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 78 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #13,528 of 35,345 by trust here, #9,968 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #12,707 of 21,575 by trust here, #7,657 by citations
• Reinforcement Learning — 30 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Generative Models — 23 citations into it; #1,172 of 1,956 by trust here, #865 by citations
• Meta & Continual Learning — 14 citations into it; #514 of 1,041 by trust here, #254 by citations
• Optimization & Training — 12 citations into it
• Biophysics — 7 citations into it

Whom Hugo Caselles-Dupré trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Timothée Lesort
5. Hanul Shin
6. Yann LeCun
7. J. Urgen Schmidhuber
8. John Schulman
9. Alex Krizhevsky
10. Alex Graves

Who trusts Hugo Caselles-Dupré overall

1. Timothée Lesort
2. Craig Atkinson
3. Kalifou René Traoré
4. Brendan McCane
5. Massimo Caccia
6. Martin Mundt
7. Oleksiy Ostapenko
8. James Smith
9. Andrei Stoian
10. David Filliat

Papers from Hugo Caselles-Dupré's perspective

Ordered by how strongly the researchers Hugo Caselles-Dupré trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Generative Models from the perspective of Continual Learning (2018) — 171 citations
• Continual State Representation Learning for Reinforcement Learning using Generative Replay (2018) — 20 citations
• DisCoRL: Continual Reinforcement Learning via Policy Distillation (2019) — 66 citations
• S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay (2019) — 18 citations