Noga Alon
Computational Theory and Mathematics · 1993–2019
A researcher in the Science corpus: 19 papers; trusts 25 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Computational Theory and Mathematics — 17 citations into it; #1,458 of 12,182 by trust here, #2,689 by citations
- Artificial Intelligence — 15 citations into it; #21,410 of 35,345 by trust here, #25,040 by citations
- Discrete Mathematics and Combinatorics — 13 citations into it; #765 of 2,017 by trust here, #386 by citations
- Algorithms & Complexity — 7 citations into it
- Statistical Learning Theory — 6 citations into it
- Mathematical Physics — 6 citations into it; #2,544 of 8,562 by trust here, #2,038 by citations
- Privacy & Security — 6 citations into it
- Statistics and Probability — 6 citations into it; #3,309 of 12,932 by trust here, #1,123 by citations
Whom Noga Alon trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vladimir Naumovich Vapnik
- J. Håstad
- David Haussler
- William T. Gowers
- Cynthia Dwork
- Jorma Rissanen
- Frank McSherry
- Michel X. Goemans
- Shai Shalev-Shwartz
- Amos Beimel
Who trusts Noga Alon overall
- Ian M. Wanless
- Olivier Bousquet
- Shahar Mendelson
- Svante Janson
- Roman Karasev
- Tong Zhang
- Jian Ding
- Daniel A. Spielman
- Alexander Olshevsky
- Roberto Imbuzeiro Oliveira
Papers from Noga Alon's perspective
Ordered by how strongly the researchers Noga Alon trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- On the concentration of eigenvalues of random symmetric matrices (2000) — 164 citations
- Approximating the cut-norm via Grothendieck's inequality (2004) — 360 citations
- Limits of Private Learning with Access to Public Data (2019) — 58 citations
- Non-backtracking random walks mix faster (2006) — 192 citations
- Algorithmic construction of sets for k-restrictions (2006) — 314 citations
- The Probabilistic Method, Second Edition (2004) — 850 citations
- Additive latin transversals (2000) — 57 citations
- Random subgraphs of properly edge-coloured complete graphs and long rainbow cycles (2016) — 49 citations
- Many T copies in H-free graphs (2015) — 313 citations
- Graphs with integral spectrum (2009) — 104 citations
- The Probabilistic Method: Alon/Probabilistic (2008) — 452 citations
- Large sets in finite fields are sumsets (2007) — 50 citations
- Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices (2004) — 135 citations
- The Number of Edge Colorings with no Monochromatic Cliques (2004) — 83 citations
- Wiley‐Interscience Series in Discrete Mathematics and Optimization (2004) — 78 citations
- Graph Products, Fourier Analysis and Spectral Techniques (2004) — 96 citations
- Percolation on finite graphs and isoperimetric inequalities (2002) — 152 citations
- Efficient Testing of Large Graphs (2000) — 384 citations
- Scale-sensitive dimensions, uniform convergence, and learnability (1993)