Shachar Fleishman

Computer Vision and Pattern Recognition · 2001–2019

A researcher in the Science corpus: 5 papers; trusts 212 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #9,381 of 21,575 by trust here, #7,027 by citations
• Computational Mechanics — 46 citations into it; #1,244 of 10,001 by trust here, #962 by citations
• Computer Graphics and Computer-Aided Design — 29 citations into it; #211 of 1,584 by trust here, #227 by citations
• Artificial Intelligence — 27 citations into it
• Generative Models — 14 citations into it
• Graph Learning — 12 citations into it
• 3D Vision & Pose — 11 citations into it
• Optimization & Training — 5 citations into it

Whom Shachar Fleishman trusts overall

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

1. Gabriel Taubin
2. David Levin
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Tim Salimans
6. Brian Curless
7. Ian J. Goodfellow
8. Alex Krizhevsky
9. Yoshua Bengio
10. Chih-Jen Lin

Who trusts Shachar Fleishman overall

1. M. Berger
2. Gaël Guennebaud
3. Jingdong Wang
4. Radu Bogdan Rusu
5. Hui Huang
6. Julie Digne
7. Sagi Katz
8. Lequan Yu
9. Quentin Mérigot
10. Markus H. Gross

Papers from Shachar Fleishman's perspective

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

Their papers

• Point set surfaces (2001) — 892 citations
• Computing and Rendering Point Set Surfaces (2003) — 1,208 citations
• Novelty Detection with GAN (2018) — 57 citations
• MeshCNN: a network with an edge (2019) — 375 citations
• Robust moving least-squares fitting with sharp features (2005) — 687 citations