Thomas Wiatowski

Computer Vision and Pattern Recognition · 2015

A researcher in the Science corpus: 1 paper; trusts 61 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 25 citations into it; #10,160 of 21,575 by trust here, #13,815 by citations
• Artificial Intelligence — 12 citations into it
• Signal Processing — 8 citations into it
• Model Architectures — 7 citations into it
• Applied Mathematics — 5 citations into it; #3,342 of 7,620 by trust here, #2,707 by citations
• Representation & Self-Supervised Learning — 5 citations into it

Whom Thomas Wiatowski trusts overall

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

1. Stéphane Mallat
2. Yann LeCun
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ingrid Daubechies
6. John F. Canny
7. Loukas Grafakos
8. David G. Lowe
9. Fu Jie Huang
10. Radford M. Neal

Who trusts Thomas Wiatowski overall

1. Philipp Christian Petersen
2. Dongmian Zou
3. Youcheng Sun
4. Radu V. Balan
5. Alberto Bietti
6. Felix Voigtländer
7. Jure Sokolić
8. C.-C. Jay Kuo
9. Maneesh Kumar Singh
10. Min Wu

Papers from Thomas Wiatowski's perspective

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

Their papers

• A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction (2015) — 400 citations