Dmitry Yarotsky

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 54 researchers and is trusted by 67 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #2,433 of 35,345 by trust here, #10,207 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Model Architectures — 14 citations into it; #581 of 2,278 by trust here, #1,855 by citations
• Scaling & Learning Theory — 14 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Dmitry Yarotsky trusts overall

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

1. Hrushikesh Narhar Mhaskar
2. Peter L. Bartlett
3. Johannes Schmidt-Hieber
4. Martin H. Anthony
5. Matus Telgarsky
6. Yann LeCun
7. Tomaso A. Poggio
8. Yoshua Bengio
9. Kaiming He
10. Geoffrey E. Hinton

Who trusts Dmitry Yarotsky overall

1. Johannes Schmidt-Hieber
2. Tengyuan Liang
3. Max H. Farrell
4. Ronald A. DeVore
5. Taiji Suzuki
6. Ding-Xuan Zhou
7. Hongzhou Lin
8. Haggai Maron
9. Shiyu Liang
10. Sanjog Misra

Papers from Dmitry Yarotsky's perspective

Ordered by how strongly the researchers Dmitry Yarotsky 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Universal Approximations of Invariant Maps by Neural Networks (2018) — 249 citations
• Optimal approximation of continuous functions by very deep ReLU networks (2018) — 339 citations
• Error bounds for approximations with deep ReLU networks (2016) — 1,527 citations