Nicholay Topin

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 7 papers; trusts 142 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #6,254 of 35,345 by trust here, #9,170 by citations
• Optimization & Training — 54 citations into it; #726 of 2,601 by trust here, #1,134 by citations
• Reinforcement Learning — 50 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it; #5,146 of 21,575 by trust here, #8,502 by citations
• Scaling & Learning Theory — 17 citations into it
• Control and Systems Engineering — 10 citations into it
• Management Science and Operations Research — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it

Whom Nicholay Topin trusts overall

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

1. James Martens
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Leslie N. Smith
6. Yann LeCun
7. L. Eon Bottou
8. Matthew D. Zeiler
9. Kaiming He
10. Alex Krizhevsky

Who trusts Nicholay Topin overall

1. Diganta Misra
2. Shuhao Cao
3. Brady Zhou
4. Eric Wong
5. Jaewon Kim
6. Lahav Lipson
7. Chunyuan Li
8. Arsenii Ashukha
9. Ross Irwin
10. Tom George Grigg

Papers from Nicholay Topin's perspective

Ordered by how strongly the researchers Nicholay Topin 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates (2017) — 524 citations
• The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors (2021) — 73 citations
• MineRL: A Large-Scale Dataset of Minecraft Demonstrations (2019) — 305 citations
• Super-convergence: very fast training of neural networks using large learning rates (2018) — 1,797 citations
• Exploring loss function topology with cyclical learning rates (2017) — 27 citations
• Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy (2019) — 57 citations
• Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes (2015) — 34 citations