J. Feldman

Computer Vision and Pattern Recognition · with Elgammal · 2000–2016

A researcher in the Science corpus: 6 papers; trusts 80 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 29 citations into it
• Artificial Intelligence — 27 citations into it; #7,888 of 35,345 by trust here, #13,353 by citations
• Cognitive Neuroscience — 19 citations into it; #1,749 of 10,852 by trust here, #5,162 by citations
• Cognitive Science & Psychology — 13 citations into it
• Neuroscience — 7 citations into it
• Statistical Learning Theory — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom J. Feldman trusts overall

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

1. John K. Kruschke
2. Ray J. Solomonoff
3. Michael I. Posner
4. Gregory J. Chaitin
5. N. Chater
6. Jorma Rissanen
7. Bradley C. Love
8. Douglas L. Medin
9. Josh Tenenbaum
10. Yoshua Bengio

Who trusts J. Feldman overall

1. Sahila Chopra
2. Isaac Lage
3. Noah D. Goodman
4. Tomer D. Ullman
5. Andreas Holzinger
6. Giulio Tononi
7. Michael Henry Tessler
8. Josh Tenenbaum
9. N. Chater
10. James P. Spillane

Papers from J. Feldman's perspective

Ordered by how strongly the researchers J. Feldman 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. ImageNet: A large-scale hierarchical image database
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• A Rational Analysis of Rule-Based Concept Learning (2008) — 448 citations
• Minimization of Boolean complexity in human concept learning (2000) — 505 citations
• Perception of Animacy from the Motion of a Single Object (2000) — 453 citations
• A catalog of Boolean concepts (2003) — 78 citations
• The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks (2016) — 16 citations
• A Century of Gestalt Psychology in Visual Perception II. Conceptual and Theoretical Foundations (2012) — 557 citations