Yali Amit

Computer Vision and Pattern Recognition · 1997–2020

A researcher in the Science corpus: 6 papers; trusts 393 researchers and is trusted by 776 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 89 citations into it; #1,551 of 21,575 by trust here, #4,137 by citations
• Generative Models — 51 citations into it; #1,145 of 1,956 by trust here, #1,875 by citations
• Artificial Intelligence — 41 citations into it; #24,586 of 35,345 by trust here, #32,362 by citations
• Probabilistic & Bayesian Methods — 19 citations into it
• Signal Processing — 14 citations into it
• Statistical and Nonlinear Physics — 10 citations into it
• Model Architectures — 6 citations into it
• Media Technology — 5 citations into it; #688 of 3,621 by trust here, #2,820 by citations

Whom Yali Amit trusts overall

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

1. David G. Lowe
2. Yann LeCun
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Pedro F. Felzenszwalb
6. Diederik P. Kingma
7. Kaiming He
8. Ross B. Girshick
9. Rob Fergus
10. Shaoqing Ren

Who trusts Yali Amit overall

1. Gérard Biau
2. Vincent Lepetit
3. Jamie Shotton
4. Nicolai Meinshausen
5. Mustafa Özuysal
6. Pascal Fua
7. Antonio Criminisi
8. Peter Buhlmann
9. Gilles Blanchard
10. Juergen Gall

Papers from Yali Amit's perspective

Ordered by how strongly the researchers Yali Amit 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Shape Quantization and Recognition with Randomized Trees (1997)
• 2D Object Detection and Recognition (2002) — 126 citations
• Generative Latent Flow: A Framework for Non-adversarial Image Generation (2019) — 15 citations
• Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy (2020) — 8 citations
• Object Detection (2020) — 596 citations
• Inferring learning rules from distribution of firing rates in cortical neurons (2015) — 110 citations