Yoni Kasten

Computer Vision and Pattern Recognition · with Basri · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 162 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 85 citations into it; #14,833 of 21,575 by trust here, #10,010 by citations
• Artificial Intelligence — 70 citations into it; #20,743 of 35,345 by trust here, #17,776 by citations
• Computational Mechanics — 42 citations into it; #6,737 of 10,001 by trust here, #2,857 by citations
• Computer Graphics and Computer-Aided Design — 39 citations into it; #1,055 of 1,584 by trust here, #579 by citations
• 3D Vision & Pose — 36 citations into it
• Scaling & Learning Theory — 34 citations into it; #292 of 428 by trust here, #329 by citations
• Optimization & Training — 26 citations into it
• Statistical Learning Theory — 19 citations into it; #809 of 1,038 by trust here, #715 by citations

Whom Yoni Kasten trusts overall

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

1. Francis R. Bach
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Sanjeev Arora
6. Yoshua Bengio
7. Kaiming He
8. M. Kazhdan
9. Yann LeCun
10. Johannes L. Schönberger

Who trusts Yoni Kasten overall

1. Songyou Peng
2. Matthew Tancik
3. Jonathan T. Barron
4. Lior Yariv
5. Zehao Yu
6. Amnon Geifman
7. Ben Mildenhall
8. Yuval Belfer
9. Qiancheng Fu
10. Eric Chan

Papers from Yoni Kasten's perspective

Ordered by how strongly the researchers Yoni Kasten trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Volume Rendering of Neural Implicit Surfaces (2021) — 1,381 citations
• The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies (2019) — 267 citations
• Frequency Bias in Neural Networks for Input of Non-Uniform Density (2020) — 249 citations
• On the Similarity between the Laplace and Neural Tangent Kernels (2020) — 116 citations
• Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance (2020) — 997 citations