Kevin Scaman

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 64 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #8,695 of 35,345 by trust here, #10,667 by citations
• Optimization & Training — 51 citations into it; #1,701 of 2,601 by trust here, #1,237 by citations
• Computer Networks and Communications — 42 citations into it; #5,041 of 9,886 by trust here, #2,424 by citations
• Computational Mechanics — 31 citations into it; #5,446 of 10,001 by trust here, #2,447 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Mathematical Optimization — 11 citations into it
• Management Science and Operations Research — 8 citations into it
• ML Systems & Hardware — 8 citations into it

Whom Kevin Scaman trusts overall

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

1. Sébastien Bubeck
2. Angelia Nedić
3. Yurii E. Nesterov
4. Wei Shi
5. John C. Duchi
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Christian Szegedy
9. Asuman E. Ozdaglar
10. Sergey Ioffe

Who trusts Kevin Scaman overall

1. Matt Jordan
2. Anastasia Koloskova
3. Amirhossein Reisizadeh
4. Hyunjik Kim
5. Wei Wang
6. Dongmian Zou
7. A. Dimakis
8. Giovanni Neglia
9. Krishna Pillutla
10. Guanghui Lan

Papers from Kevin Scaman's perspective

Ordered by how strongly the researchers Kevin Scaman 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Lipschitz regularity of deep neural networks: analysis and efficient estimation (2018) — 703 citations
• Optimal Algorithms for Non-Smooth Distributed Optimization in Networks (2018) — 177 citations
• Optimal Convergence Rates for Convex Distributed Optimization in Networks (2019) — 90 citations
• Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks (2017) — 372 citations