Nir Shavit

Computer Vision and Pattern Recognition · 2004–2020

A researcher in the Science corpus: 9 papers; trusts 552 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 111 citations into it; #9,664 of 21,575 by trust here, #8,722 by citations
• Artificial Intelligence — 102 citations into it; #14,822 of 35,345 by trust here, #7,346 by citations
• Efficiency & Compression — 34 citations into it
• Model Architectures — 24 citations into it
• Computer Networks and Communications — 23 citations into it; #2,059 of 9,886 by trust here, #3,993 by citations
• Generative Models — 23 citations into it
• Optimization & Training — 23 citations into it
• Evaluation & Benchmarks — 19 citations into it

Whom Nir Shavit trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Ross B. Girshick
10. Jimmy Ba

Who trusts Nir Shavit overall

1. Danny Hendler
2. Håkan Grahn
3. T. Harris
4. Yochai Blau
5. Chi Cao Minh
6. Michael Tschannen
7. Andrew Jones
8. Eirikur Agustsson
9. Itai Incze

Papers from Nir Shavit's perspective

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

Their papers

• A Constructive Prediction of the Generalization Error Across Scales (2019) — 278 citations
• Generative Compression (2017) — 200 citations
• Deep Learning is Robust to Massive Label Noise (2017) — 639 citations
• On the Predictability of Pruning Across Scales (2020) — 48 citations
• Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference (2020)
• The art of multiprocessor programming by Maurice Herlihy and Nir Shavit (2011) — 562 citations
• Flat combining and the synchronization-parallelism tradeoff (2010) — 354 citations
• Transactional Locking II (2006) — 1,026 citations
• A scalable lock-free stack algorithm (2004) — 303 citations