Sivan Sabato
Statistics and Probability · 2008–2013
A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 25 citations into it
- Artificial Intelligence — 23 citations into it; #24,087 of 35,345 by trust here, #19,439 by citations
- Statistical Learning Theory — 14 citations into it
- Management Science and Operations Research — 10 citations into it
- Computational Mechanics — 9 citations into it
- Applied Mathematics — 8 citations into it
- Optimization & Training — 8 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it; #14,996 of 21,575 by trust here, #19,766 by citations
Whom Sivan Sabato trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vladimir Naumovich Vapnik
- Peter L. Bartlett
- Martin H. Anthony
- Shahar Mendelson
- Tong Zhang
- Corinna Cortes
- Avrim Blum
- Philip M. Long
- Oded Maron
- Robert E. Schapire
Who trusts Sivan Sabato overall
- Stanislav Minsker
- Zoltán Szabó
- Dmitrii Ostrovskii
- Wenlong Mou
- Francis R. Bach
- Krishna Pillutla
- Gábor Lugosi
- Bharath K. Sriperumbudur
- Dong Yin
- Adarsh Prasad
Papers from Sivan Sabato's perspective
Ordered by how strongly the researchers Sivan Sabato trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
Their papers
- Learning and generalization with the information bottleneck (2008) — 251 citations
- Loss Minimization and Parameter Estimation with Heavy Tails (2013) — 200 citations
- Multi-instance learning with any hypothesis class (2011) — 49 citations
- Tight Sample Complexity of Large-Margin Learning (2010) — 9 citations