Or Sharir

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 189 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #12,059 of 35,345 by trust here, #8,157 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Model Architectures — 41 citations into it
• Natural Language Processing — 39 citations into it
• Large Language Models — 22 citations into it
• Scaling & Learning Theory — 18 citations into it
• Optimization & Training — 15 citations into it
• Computational Mathematics — 11 citations into it; #196 of 612 by trust here, #118 by citations

Whom Or Sharir trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Jimmy Ba
6. Ilya Sutskever
7. Kaiming He
8. Christian Szegedy
9. Razvan Pascanu
10. Yann LeCun

Who trusts Or Sharir overall

1. Jean Kossaifi
2. Nadav Cohen
3. Amnon Shashua
4. Spurthi Amba Hombaiah
5. Yoav Levine
6. Noam Wies
7. Zachary Chase Lipton
8. J. Trinquier
9. Noam Razin
10. Rose Yu

Papers from Or Sharir's perspective

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

Their papers

• On the Expressive Power of Deep Learning: A Tensor Analysis (2015) — 493 citations
• The Cost of Training NLP Models: A Concise Overview (2020) — 238 citations
• Limits to Depth Efficiencies of Self-Attention (2020) — 56 citations
• SenseBERT: Driving Some Sense into BERT (2019) — 203 citations
• Deep autoregressive models for the efficient variational simulation of many-body quantum systems (2019) — 252 citations
• The Depth-to-Width Interplay in Self-Attention. (2020) — 33 citations
• On the Expressive Power of Overlapping Architectures of Deep Learning (2017) — 45 citations