Shira Kritchman

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #9,183 of 35,345 by trust here, #14,181 by citations
• Statistics and Probability — 26 citations into it; #2,915 of 12,932 by trust here, #4,964 by citations
• Scaling & Learning Theory — 21 citations into it; #277 of 428 by trust here, #329 by citations
• Optimization & Training — 20 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Model Architectures — 9 citations into it
• Computational Mechanics — 8 citations into it; #4,987 of 10,001 by trust here, #6,877 by citations
• Statistical Learning Theory — 8 citations into it; #776 of 1,038 by trust here, #715 by citations

Whom Shira Kritchman trusts overall

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

1. Iain M. Johnstone
2. Boaz Nadler
3. B. Everitt
4. James R. Schott
5. Tom Minka
6. Noureddine El Karoui
7. Paul D. Sampson
8. Wojtek Janusz Krzanowski
9. Debashis Paul
10. Jinho Baik

Who trusts Shira Kritchman overall

1. A. Onatski
2. Debashis Paul
3. Art B. Owen
4. Matan Gavish
5. Yunjin Choi
6. Anil Jayanti Aswani
7. Boaz Nadler
8. Marcelo J. Moreira
9. Alexander Aue
10. J. Wang

Papers from Shira Kritchman's perspective

Ordered by how strongly the researchers Shira Kritchman 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. Learning Multiple Layers of Features from Tiny Images
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
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. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Their papers

• 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
• Determining the number of components in a factor model from limited noisy data (2008) — 234 citations
• Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory (2009) — 318 citations