Balaraman Ravindran

Artificial Intelligence · with Barto · 2001–2023

A researcher in the Science corpus: 14 papers; trusts 380 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 174 citations into it; #7,644 of 35,345 by trust here, #6,468 by citations
• Reinforcement Learning — 164 citations into it; #839 of 2,242 by trust here, #992 by citations
• Computer Vision and Pattern Recognition — 73 citations into it; #10,939 of 21,575 by trust here, #15,955 by citations
• Control and Systems Engineering — 31 citations into it
• Model Architectures — 26 citations into it; #1,437 of 2,278 by trust here, #2,009 by citations
• Natural Language Processing — 25 citations into it
• Computational Theory and Mathematics — 22 citations into it
• Management Science and Operations Research — 22 citations into it

Whom Balaraman Ravindran trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Volodymyr Mnih
4. Andrew Barto
5. Yoshua Bengio
6. Yann LeCun
7. Burr Settles
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Alex Graves

Who trusts Balaraman Ravindran overall

1. Jinglin Chen
2. Robert W. Platt
3. Nan Jiang
4. Anuj Mahajan
5. Yu Zhang
6. Matteo Hessel
7. Matthew E. Taylor
8. B. '. Pires
9. Michael Crawshaw
10. Aravind Rajeswaran

Papers from Balaraman Ravindran's perspective

Ordered by how strongly the researchers Balaraman Ravindran 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• EPOpt: Learning Robust Neural Network Policies Using Model Ensembles (2016) — 400 citations
• An Autoencoder Approach to Learning Bilingual Word Representations (2014) — 348 citations
• Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks (2018) — 68 citations
• Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning (2017) — 101 citations
• Dynamic Action Repetition for Deep Reinforcement Learning (2017) — 80 citations
• RAIL: Risk-Averse Imitation Learning (2017) — 21 citations
• Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning (2017) — 37 citations
• Deep Reinforcement Learning (2023) — 596 citations
• Learning to Multi-Task by Active Sampling (2017) — 40 citations
• Online Multi-Task Learning Using Active Sampling (2017) — 21 citations
• Changes in earthworm gut associated enzymes and microbial diversity on the treatment of fermented tannery waste using epigeic earthworm Eudrilus eugeniae (2015) — 100 citations
• Activity Recognition for Natural Human Robot Interaction (2014) — 41 citations
• An algebraic approach to abstraction in reinforcement learning (2004) — 128 citations
• Symmetries and Model Minimization in Markov Decision Processes (2001) — 91 citations