Blake Bordelon

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; #13,116 of 35,345 by trust here, #24,417 by citations
• Scaling & Learning Theory — 44 citations into it; #234 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; #659 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 Blake Bordelon trusts overall

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

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

Who trusts Blake Bordelon overall

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

Papers from Blake Bordelon's perspective

Ordered by how strongly the researchers Blake Bordelon 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. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
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