William F. Whitney

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 9 papers; trusts 739 researchers and is trusted by 1,109 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it; #9,756 of 35,345 by trust here, #19,018 by citations
• Reinforcement Learning — 111 citations into it; #1,442 of 2,242 by trust here, #1,663 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #1,280 of 21,575 by trust here, #2,347 by citations
• Generative Models — 35 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Computational Theory and Mathematics — 25 citations into it; #9,414 of 12,182 by trust here, #8,756 by citations
• Management Science and Operations Research — 22 citations into it
• Model Architectures — 20 citations into it

Whom William F. Whitney trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Ronald J. Williams
6. Ruslan Salakhutdinov
7. Ilya Sutskever
8. Diederik P. Kingma
9. Alex Graves
10. Wojciech Zaremba

Who trusts William F. Whitney overall

1. Francesco Locatello
2. Arunkumar Byravan
3. Tejas D. Kulkarni
4. Josh Tenenbaum
5. Sergey Levine
6. Irina Higgins
7. Michael Chang
8. Dinesh Jayaraman
9. Kristen Grauman
10. Jiajun Wu

Papers from William F. Whitney's perspective

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

Their papers

• Deep Convolutional Inverse Graphics Network (2015) — 951 citations
• Understanding Visual Concepts with Continuation Learning (2016) — 55 citations
• Model-Based Planning in Discrete Action Spaces (2017) — 17 citations
• Disentangled Representations in Neural Models (2016) — 18 citations
• Offline RL Without Off-Policy Evaluation (2021) — 205 citations
• Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning (2017) — 43 citations
• Dynamics-aware Embeddings (2019) — 59 citations
• Understanding the Asymptotic Performance of Model-Based RL Methods (2018) — 7 citations
• Model-Based Planning with Discrete and Continuous Actions (2017) — 54 citations