Kaleigh Clary

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 59 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #26,568 of 35,345 by trust here, #26,682 by citations
• Reinforcement Learning — 50 citations into it
• Safety, Robustness & Interpretability — 23 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Optimization & Training — 6 citations into it
• Management Science and Operations Research — 5 citations into it; #11,115 of 11,946 by trust here, #9,383 by citations

Whom Kaleigh Clary trusts overall

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

1. Volodymyr Mnih
2. Richard S. Sutton
3. John Schulman
4. Marc G. Bellemare
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Alex Graves
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Kaleigh Clary overall

1. Emma Tosch
2. Akanksha Atrey
3. David D. Jensen
4. John Foley

Papers from Kaleigh Clary's perspective

Ordered by how strongly the researchers Kaleigh Clary 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments (2019) — 27 citations
• Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning (2019) — 113 citations
• Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning (2019) — 9 citations
• ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents (2018) — 7 citations