Prasad Tadepalli

Artificial Intelligence · 1994–2019

A researcher in the Science corpus: 18 papers; trusts 90 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #5,547 of 35,345 by trust here, #5,682 by citations
• Reinforcement Learning — 71 citations into it; #536 of 2,242 by trust here, #644 by citations
• Natural Language Processing — 29 citations into it; #3,150 of 5,688 by trust here, #4,183 by citations
• Computer Vision and Pattern Recognition — 16 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Control and Systems Engineering — 11 citations into it; #7,228 of 8,257 by trust here, #2,376 by citations
• Management Science and Operations Research — 11 citations into it; #2,142 of 11,946 by trust here, #1,155 by citations
• Computational Theory and Mathematics — 9 citations into it; #2,494 of 12,182 by trust here, #1,379 by citations

Whom Prasad Tadepalli trusts overall

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

1. Kurt Driessens
2. Hendrik Blockeel
3. Andrew Barto
4. Thomas Gärtner
5. Radford M. Neal
6. J. Ramon
7. Satinder Singh
8. Richard S. Sutton
9. Dimitri P. Bertsekas
10. Andrew Y. Ng

Who trusts Prasad Tadepalli overall

1. Sridhar Mahadevan
2. Mohammad Ghavamzadeh
3. Daphne Koller
4. Geoffrey E. Hinton
5. Ronald E. Parr
6. Alessandro Lazaric
7. Jens Kober
8. Carlos Guestrin
9. David Andre
10. Nikolaos Goumagias

Papers from Prasad Tadepalli's perspective

Ordered by how strongly the researchers Prasad Tadepalli 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Multi-task reinforcement learning: a hierarchical Bayesian approach (2007) — 354 citations
• Transfer in variable-reward hierarchical reinforcement learning (2008) — 102 citations
• Optimal Policies Tend To Seek Power (2019) — 130 citations
• Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (2018) — 99 citations
• A Decision-Theoretic Model of Assistance (2007) — 130 citations
• Imitation Learning with Demonstrations and Shaping Rewards (2014) — 44 citations
• Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation (2019) — 26 citations
• Training Deep Reactive Policies for Probabilistic Planning Problems (2018) — 42 citations
• A Bayesian Approach for Policy Learning from Trajectory Preference Queries (2012) — 202 citations
• Bayesian role discovery for multi-agent reinforcement learning (2010) — 9 citations
• Adaptive Control Processes (2010) — 751 citations
• Bayesian Policy Search for Multi-Agent Role Discovery (2010) — 16 citations
• Multiagent Transfer Learning via Assignment-Based Decomposition (2009) — 5 citations
• Learning and transferring roles in multi-agent MDPs (2008) — 4 citations
• Learning and Transferring Roles in Multi-Agent Reinforcement (2008)
• Relational Reinforcement Learning An Overview (2004)
• Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function (1996)
• H-Learning: A Reinforcement Learning Method for Optimizing Undiscounted Average Reward (1994)