Murtaza Dalal

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 163 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 149 citations into it; #420 of 2,242 by trust here, #458 by citations
• Artificial Intelligence — 139 citations into it; #3,026 of 35,345 by trust here, #4,336 by citations
• Control and Systems Engineering — 57 citations into it; #1,386 of 8,257 by trust here, #359 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Computational Theory and Mathematics — 32 citations into it; #3,893 of 12,182 by trust here, #2,629 by citations
• Management Science and Operations Research — 20 citations into it
• Robotics & Control — 20 citations into it
• Electrical and Electronic Engineering — 15 citations into it

Whom Murtaza Dalal trusts overall

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

1. Richard S. Sutton
2. Sergey Levine
3. E. Todorov
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Pieter Abbeel
7. Yoshua Bengio
8. Alex Krizhevsky
9. Jan Peters
10. J. Urgen Schmidhuber

Who trusts Murtaza Dalal overall

1. Sergey Levine
2. Benjamin Eysenbach
3. Abhishek Gupta
4. Corey Lynch
5. Chelsea Finn
6. Suraj Nair
7. Michael Janner
8. Dibya Ghosh
9. Vitchyr H. Pong
10. Shixiang Shane Gu

Papers from Murtaza Dalal's perspective

Ordered by how strongly the researchers Murtaza Dalal 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Visual Reinforcement Learning with Imagined Goals (2018) — 603 citations
• Temporal Difference Models: Model-Free Deep RL for Model-Based Control (2018) — 266 citations
• Accelerating Online Reinforcement Learning with Offline Datasets (2020) — 858 citations
• Skew-Fit: State-Covering Self-Supervised Reinforcement Learning (2019) — 313 citations
• Composable Deep Reinforcement Learning for Robotic Manipulation (2018) — 254 citations