Adam R. Klivans

Artificial Intelligence · 2001–2020

A researcher in the Science corpus: 13 papers; trusts 96 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #2,899 of 35,345 by trust here, #4,860 by citations
• Computer Vision and Pattern Recognition — 49 citations into it; #10,238 of 21,575 by trust here, #11,721 by citations
• Statistical Learning Theory — 46 citations into it; #218 of 1,038 by trust here, #236 by citations
• Optimization & Training — 41 citations into it
• Model Architectures — 20 citations into it
• Computational Mechanics — 18 citations into it; #7,621 of 10,001 by trust here, #6,582 by citations
• Statistics and Probability — 17 citations into it
• Scaling & Learning Theory — 16 citations into it

Whom Adam R. Klivans trusts overall

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

1. Leslie G. Valiant
2. Yoav Freund
3. Avrim Blum
4. Nick Littlestone
5. Robert E. Schapire
6. Jeffrey C. Jackson
7. Rocco A. Servedio
8. Michael Kearns
9. Oded Regev
10. R. Rivest

Who trusts Adam R. Klivans overall

1. Vitaly Feldman
2. Oded Regev
3. Ilias Diakonikolas
4. Surbhi Goel
5. Shai Shalev-Shwartz
6. Amit Daniely
7. Yuchen Zhang
8. Philip M. Long
9. Zeyuan Allen-Zhu
10. Tong Wang

Papers from Adam R. Klivans's perspective

Ordered by how strongly the researchers Adam R. Klivans 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Halfspaces with Malicious Noise (2009) — 145 citations
• Cryptographic Hardness for Learning Intersections of Halfspaces (2006) — 197 citations
• Agnostically learning halfspaces (2005) — 366 citations
• Boosting and Hard-Core Set Construction (2003) — 59 citations
• Reliably Learning the ReLU in Polynomial Time (2016) — 131 citations
• Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection (2020) — 123 citations
• Learning DNF in time 2Õ(n1/3) (2004) — 176 citations
• Learning One Convolutional Layer with Overlapping Patches (2018) — 81 citations
• Efficient Algorithms for Outlier-Robust Regression (2018) — 171 citations
• Learning Neural Networks with Two Nonlinear Layers in Polynomial Time (2017) — 78 citations
• Agnostically learning decision trees (2008) — 79 citations
• Toward Attribute Efficient Learning of Decision Lists and Parities (2006) — 83 citations
• Learning DNF in time 2 Õ(n 1/3 ) . (2001) — 170 citations