Keith W. Ross

Artificial Intelligence · IEEE transactions on multimedia · 2000–2021

A researcher in the Science corpus: 12 papers; trusts 133 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #15,345 of 35,345 by trust here, #23,731 by citations
• Reinforcement Learning — 92 citations into it
• Computer Networks and Communications — 29 citations into it; #3,226 of 9,886 by trust here, #4,405 by citations
• Management Science and Operations Research — 22 citations into it
• Computational Theory and Mathematics — 17 citations into it; #5,850 of 12,182 by trust here, #10,901 by citations
• Electrical and Electronic Engineering — 15 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Control and Systems Engineering — 12 citations into it

Whom Keith W. Ross trusts overall

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

1. Richard S. Sutton
2. Tuomas Haarnoja
3. Longxin Lin
4. Hado van Hasselt
5. Geoffrey E. Hinton
6. E. Todorov
7. Sergey Levine
8. Diederik P. Kingma
9. David Silver
10. John Schulman

Who trusts Keith W. Ross overall

1. Yan Huang
2. Saverio Mascolo
3. Samarth Sinha
4. Youngmin Oh
5. Yen-Cheng Liu
6. Meeyoung Cha
7. Daewoo Kim

Papers from Keith W. Ross's perspective

Ordered by how strongly the researchers Keith W. Ross 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Reinforcement Learning: An Introduction
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning (2019) — 143 citations
• Randomized Ensembled Double Q-Learning: Learning Fast Without a Model (2021) — 397 citations
• Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past (2019) — 54 citations
• "On the internet, nobody knows you're a dog": a twitter case study of anonymity in social networks (2014) — 98 citations
• Computer Networking: A Top-Down Approach (6th Edition) (2012) — 388 citations
• Waiting for Anonymity: Understanding Delays in the Tor Overlay (2010) — 49 citations
• A Measurement Study of a Large-Scale P2P IPTV System (2007) — 1,140 citations
• Stochastic Fluid Theory for P2P Streaming Systems (2007) — 371 citations
• Can internet video-on-demand be profitable? (2007) — 506 citations
• Pollution in P2P file sharing systems (2005) — 357 citations
• Object replication strategies in content distribution networks (2002) — 490 citations
• Computer networking - a top-down approach featuring the internet (2000) — 686 citations