Amnon Geifman

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 41 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it
• Scaling & Learning Theory — 36 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Statistical Learning Theory — 22 citations into it
• Model Architectures — 21 citations into it
• Optimization & Training — 18 citations into it
• Statistical and Nonlinear Physics — 18 citations into it
• Statistics and Probability — 5 citations into it

Whom Amnon Geifman trusts overall

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

1. Sanjeev Arora
2. Francis R. Bach
3. Christopher K. I. Williams
4. Julien Mairal
5. Simon Shaolei Du
6. Ronen Basri
7. Alberto Bietti
8. Geoffrey E. Hinton
9. Kaiming He
10. Alex Krizhevsky

Who trusts Amnon Geifman overall

1. Lin Chen
2. Yuval Belfer
3. Soufiane Hayou
4. Sheng Xu
5. Ryo Karakida
6. Arnaud Doucet

Papers from Amnon Geifman's perspective

Ordered by how strongly the researchers Amnon Geifman 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Frequency Bias in Neural Networks for Input of Non-Uniform Density (2020) — 249 citations
• Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks (2021) — 25 citations
• On the Similarity between the Laplace and Neural Tangent Kernels (2020) — 116 citations