Yair Movshovitz-Attias

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 232 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it; #11,281 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 27 citations into it; #22,564 of 35,345 by trust here, #14,438 by citations
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it; #2,215 of 3,145 by trust here, #1,811 by citations

Whom Yair Movshovitz-Attias trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Christian Szegedy
4. Yann LeCun
5. Ilya Sutskever
6. Ian J. Goodfellow
7. Yoshua Bengio
8. Jia Deng
9. Ross B. Girshick
10. Kaiming He

Who trusts Yair Movshovitz-Attias overall

1. Hang Qi
2. Xu Zhang
3. Pegah Khosravi
4. Chaitanya Mitash
5. Andrew Zhai
6. Mingu Kang
7. Mahmut Kaya
8. Apostolia Tsirikoglou
9. Matthew A. Brown
10. Kevin Musgrave

Papers from Yair Movshovitz-Attias's perspective

Ordered by how strongly the researchers Yair Movshovitz-Attias 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• No Fuss Distance Metric Learning Using Proxies (2017) — 713 citations
• How Useful Is Photo-Realistic Rendering for Visual Learning? (2016) — 158 citations
• Ontological supervision for fine grained classification of Street View storefronts (2015) — 84 citations