Dilip Arumugam

Reinforcement Learning · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 69 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 98 citations into it
• Artificial Intelligence — 83 citations into it; #20,523 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 42 citations into it; #3,502 of 5,688 by trust here, #2,846 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #15,824 of 21,575 by trust here, #13,269 by citations
• Computational Theory and Mathematics — 12 citations into it
• Control and Systems Engineering — 10 citations into it
• Statistical Learning Theory — 9 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Dilip Arumugam 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. Yoshua Bengio
5. Andrew Barto
6. Ilya Sutskever
7. Michael L. Littman
8. Pieter Abbeel
9. Alex Krizhevsky
10. Tomas Mikolov

Who trusts Dilip Arumugam overall

1. Siddharth Karamcheti
2. Cynthia Matuszek
3. Lin Guan
4. Jan Leike
5. Joyce Yue Chai
6. Prasoon Goyal
7. Edward C. Williams
8. Stephen Casper
9. Stefanie Tellex
10. Dorsa Sadigh

Papers from Dilip Arumugam's perspective

Ordered by how strongly the researchers Dilip Arumugam 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities (2017) — 64 citations
• Grounding English Commands to Reward Functions (2015) — 104 citations
• Deep Reinforcement Learning from Policy-Dependent Human Feedback (2019) — 111 citations
• A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions (2017) — 17 citations
• Value Preserving State-Action Abstractions (2020) — 59 citations
• State Abstractions for Lifelong Reinforcement Learning (2018) — 140 citations
• Toward Good Abstractions for Lifelong Learning (2017)