Haim Avron

Artificial Intelligence · 2011–2018

A researcher in the Science corpus: 9 papers; trusts 183 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #5,470 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #16,498 of 21,575 by trust here, #20,756 by citations
• Statistical Learning Theory — 27 citations into it
• Optimization & Training — 23 citations into it
• Computational Mechanics — 22 citations into it; #1,630 of 10,001 by trust here, #4,039 by citations
• Statistics and Probability — 20 citations into it; #2,715 of 12,932 by trust here, #4,964 by citations
• Computational Theory and Mathematics — 19 citations into it; #5,137 of 12,182 by trust here, #5,724 by citations
• Representation & Self-Supervised Learning — 11 citations into it; #2,046 of 3,145 by trust here, #2,178 by citations

Whom Haim Avron trusts overall

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

1. Christopher K. I. Williams
2. L. Eon Bottou
3. Geoffrey E. Hinton
4. Quoc V. Le
5. Bernhard Schölkopf
6. Alex Smola
7. Alex Krizhevsky
8. Francis R. Bach
9. Ali Rahimi
10. Yoshua Bengio

Who trusts Haim Avron overall

1. Xiangru Lian
2. Ji Liu
3. Francis R. Bach
4. David Lopez-Paz
5. Vikas Sindhwani
6. Jiyan Yang
7. Zi-Mei Yao
8. David P. Woodruff
9. Aditya Ramesh
10. Michael W. Mahoney

Papers from Haim Avron's perspective

Ordered by how strongly the researchers Haim Avron 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Kernel methods match Deep Neural Networks on TIMIT (2014) — 131 citations
• Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix (2011) — 409 citations
• Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization (2013) — 94 citations
• Random Laplace Feature Maps for Semigroup Kernels on Histograms (2014) — 54 citations
• Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations (2016) — 24 citations
• Stochastic Chebyshev Gradient Descent for Spectral Optimization (2018) — 11 citations
• Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels (2014) — 178 citations
• Subspace Embeddings for the Polynomial Kernel (2014) — 104 citations
• High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization (2014) — 24 citations