Anuj Mahajan

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 163 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 193 citations into it; #14,274 of 35,345 by trust here, #10,942 by citations
• Reinforcement Learning — 165 citations into it; #1,422 of 2,242 by trust here, #1,100 by citations
• Management Science and Operations Research — 31 citations into it
• Computational Theory and Mathematics — 30 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Control and Systems Engineering — 22 citations into it
• Optimization & Training — 19 citations into it
• Game & Decision Theory — 15 citations into it

Whom Anuj Mahajan trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Radford M. Neal
5. Yoshua Bengio
6. Sergey Levine
7. Andrew Y. Ng
8. Ronald J. Williams
9. J. Urgen Schmidhuber
10. Alex Krizhevsky

Who trusts Anuj Mahajan overall

1. Matthew R. Fellows
2. Tonghan Wang
3. Tabish Rashid
4. Shimon Whiteson
5. Christian Schröder de Witt
6. Yaodong Yang
7. Tarun Gupta
8. Chenghao Li
9. Yihan Wang
10. Tim G. J. Rudner

Papers from Anuj Mahajan's perspective

Ordered by how strongly the researchers Anuj Mahajan 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. 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

• Open-Ended Learning Leads to Generally Capable Agents (2021) — 230 citations
• VIREL: A Variational Inference Framework for Reinforcement Learning (2018) — 59 citations
• MAVEN: Multi-Agent Variational Exploration (2019) — 444 citations
• RODE: Learning Roles to Decompose Multi-Agent Tasks (2020) — 286 citations
• Symmetry Learning for Function Approximation in Reinforcement Learning (2017) — 32 citations
• Symmetry Detection and Exploitation for Function Approximation in Deep RL (2017) — 12 citations