Eric Balkanski

Artificial Intelligence · 2018

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it; #7,463 of 35,345 by trust here, #25,040 by citations
• Computational Theory and Mathematics — 16 citations into it; #1,736 of 12,182 by trust here, #6,798 by citations
• Computer Networks and Communications — 9 citations into it; #1,701 of 9,886 by trust here, #7,492 by citations
• Optimization & Training — 9 citations into it
• Algorithms & Complexity — 7 citations into it; #318 of 516 by trust here, #470 by citations
• Computer Graphics and Computer-Aided Design — 7 citations into it
• Statistical and Nonlinear Physics — 6 citations into it
• Information Systems — 5 citations into it

Whom Eric Balkanski trusts overall

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

1. Yaron Singer
2. Alex Krizhevsky
3. Amit Chakrabarti
4. Jaewon Yang
5. Alina Ene
6. Uriel Feige
7. F. Maxwell Harper
8. George L. Nemhauser
9. Olga G. Troyanskaya
10. Jure Leskovec

Who trusts Eric Balkanski overall

1. Sharon Qian
2. Yaron Singer
3. Jesse Vig
4. Matthew Fahrbach
5. Sebastian Gehrmann
6. Vahab S. Mirrokni
7. Alina Ene
8. Yonatan Belinkov
9. Adam Breuer
10. Morteza Zadimoghaddam

Papers from Eric Balkanski's perspective

Ordered by how strongly the researchers Eric Balkanski trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning Multiple Layers of Features from Tiny Images
2. An analysis of approximations for maximizing submodular set functions—I
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Best Algorithms for Approximating the Maximum of a Submodular Set Function
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Non-monotone Submodular Maximization in Exponentially Fewer Iterations (2018) — 61 citations
• The adaptive complexity of maximizing a submodular function (2018) — 136 citations
• Approximation Guarantees for Adaptive Sampling (2018) — 30 citations