Abdulkadir Canatar

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 50 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #13,114 of 35,345 by trust here, #24,417 by citations
• Scaling & Learning Theory — 44 citations into it; #232 of 428 by trust here, #363 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Statistical Learning Theory — 15 citations into it; #657 of 1,038 by trust here, #874 by citations
• Statistics and Probability — 13 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 9 citations into it

Whom Abdulkadir Canatar trusts overall

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

1. Radford M. Neal
2. Peter Sollich
3. Grace Wahba
4. Christopher K. I. Williams
5. Yuhai Wu
6. Jaehoon Lee
7. Carl E. Rasmussen
8. Arthur Jacot
9. Madhu S. Advani
10. E. Gardner

Who trusts Abdulkadir Canatar overall

1. Ryo Karakida
2. Alessandro Favero
3. Shotaro Akaho
4. Francesco Cagnetta
5. Matthieu Wyart
6. Stefano Spigler
7. Yasaman Bahri
8. Yuan Cao
9. Amnon Geifman
10. Bruno Loureiro

Papers from Abdulkadir Canatar's perspective

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

Their papers

• Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks (2020) — 257 citations
• Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks (2020) — 269 citations