David Gadot

Computer Vision and Pattern Recognition · 2015

A researcher in the Science corpus: 1 paper; trusts 225 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 23 citations into it; #5,990 of 21,575 by trust here, #15,529 by citations
• Model Architectures — 7 citations into it
• Media Technology — 6 citations into it

Whom David Gadot trusts overall

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

1. Yann LeCun
2. Jure Zbontar
3. Matthew D. Zeiler
4. Karen Simonyan
5. Sergey Ioffe
6. Gunnar Farnebäck
7. Piotr Doll'ar
8. Geoffrey E. Hinton
9. Andrew Zisserman
10. Alex Krizhevsky

Who trusts David Gadot overall

1. Simon Niklaus
2. Junhwa Hur
3. Fatma Güney
4. Long Mai
5. Simon Meister
6. Feng Liu
7. Stefan Roth
8. René Schuster
9. Till Kroeger
10. Andreas Geiger

Papers from David Gadot's perspective

Ordered by how strongly the researchers David Gadot 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• PatchBatch: A Batch Augmented Loss for Optical Flow (2015) — 105 citations