Clare Lyle

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 60 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 129 citations into it; #35,292 of 35,345 by trust here, #9,092 by citations
• Reinforcement Learning — 70 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Computational Theory and Mathematics — 19 citations into it
• Management Science and Operations Research — 17 citations into it
• Signal Processing — 11 citations into it
• Computer Networks and Communications — 10 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it

Whom Clare Lyle trusts overall

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

1. Marc G. Bellemare
2. Will Dabney
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Mohit Sewak
6. Hermann Thorisson
7. Yoshua Bengio
8. Alex Krizhevsky
9. Diederik P. Kingma
10. R´emi Munos

Who trusts Clare Lyle overall

1. Jean Harb
2. Rishabh Agarwal
3. Amy Zhang
4. Tom Schaul

Papers from Clare Lyle's perspective

Ordered by how strongly the researchers Clare Lyle 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Invariant Causal Prediction for Block MDPs (2020) — 159 citations
• The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (2018) — 923 citations
• A Geometric Perspective on Optimal Representations for Reinforcement Learning (2019) — 99 citations
• A Comparative Analysis of Expected and Distributional Reinforcement Learning (2019) — 97 citations
• Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search (2020) — 16 citations