Grant M. Rotskoff

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it
• Statistical and Nonlinear Physics — 24 citations into it
• Statistics and Probability — 23 citations into it
• Optimization & Training — 21 citations into it
• Atomic and Molecular Physics, and Optics — 12 citations into it
• Statistical Learning Theory — 9 citations into it
• Model Architectures — 9 citations into it
• Scaling & Learning Theory — 9 citations into it; #109 of 428 by trust here, #241 by citations

Whom Grant M. Rotskoff trusts overall

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

1. Sylvia Serfaty
2. Thomas Leblé
3. Francis R. Bach
4. George V. Cybenko
5. Yann LeCun
6. L. Eon Bottou
7. Andrew R. Barron
8. Antonio Auffinger
9. J. Behler
10. Jens Berg

Who trusts Grant M. Rotskoff overall

1. Samet Oymak
2. Greg Yang
3. Xialiang Dou
4. Justin A. Sirignano
5. Mario Geiger
6. Lénaïc Chizat
7. Colin Wei
8. Song Mei
9. Jason D. Lee
10. Boris Hanin

Papers from Grant M. Rotskoff's perspective

Ordered by how strongly the researchers Grant M. Rotskoff 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. Learning Multiple Layers of Features from Tiny Images
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. Attention is All you Need (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks (2018) — 144 citations
• Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error (2018) — 214 citations
• Trainability and Accuracy of Artificial Neural Networks: An Interacting Particle System Approach (2018) — 170 citations
• Near-optimal protocols in complex nonequilibrium transformations (2016) — 53 citations