Victor Bapst

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 11 papers; trusts 189 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 281 citations into it; #9,678 of 35,345 by trust here, #2,853 by citations
• Computer Vision and Pattern Recognition — 151 citations into it; #16,928 of 21,575 by trust here, #6,606 by citations
• Reinforcement Learning — 141 citations into it; #1,138 of 2,242 by trust here, #303 by citations
• Representation & Self-Supervised Learning — 42 citations into it
• Information Systems — 24 citations into it
• Control and Systems Engineering — 20 citations into it
• Economics and Econometrics — 10 citations into it

Whom Victor Bapst trusts overall

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

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

Who trusts Victor Bapst overall

1. Sergey Levine
2. Sjoerd van Steenkiste
3. Yoshua Bengio
4. Shangtong Zhang
5. Anirudh Goyal
6. Peter W. Battaglia
7. Alvaro Sanchez-Gonzalez
8. Ruslan Salakhutdinov
9. Bernhard Schölkopf
10. Kelsey R. Allen

Papers from Victor Bapst's perspective

Ordered by how strongly the researchers Victor Bapst 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Sample Efficient Actor-Critic with Experience Replay (2016) — 821 citations
• Distral: Robust multitask reinforcement learning (2017) — 628 citations
• Relational Deep Reinforcement Learning (2018) — 245 citations
• Generating Interpretable Images with Controllable Structure (2017) — 65 citations
• Relational inductive bias for physical construction in humans and machines (2018) — 122 citations
• Hyperbolic Attention Networks (2018) — 276 citations
• Structured agents for physical construction (2019) — 107 citations
• Hamiltonian Graph Networks with ODE Integrators (2019) — 195 citations
• Unveiling the predictive power of static structure in glassy systems (2020) — 349 citations
• Relational inductive biases, deep learning, and graph networks (2018) — 3,656 citations
• Deep reinforcement learning with relational inductive biases (2018) — 231 citations