Matthew Eichhorn

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Model Architectures — 18 citations into it
• Efficiency & Compression — 17 citations into it
• Signal Processing — 15 citations into it
• Optimization & Training — 6 citations into it; #1,943 of 2,601 by trust here, #2,162 by citations

Whom Matthew Eichhorn trusts overall

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

1. Yoshua Bengio
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Quoc V. Le
9. Alex Graves
10. Ross B. Girshick

Who trusts Matthew Eichhorn overall

1. Tri Dao
2. Beidi Chen
3. Albert Gu

Papers from Matthew Eichhorn's perspective

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

Their papers

• Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps (2020) — 56 citations
• Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations (2019) — 131 citations