Wesley J. Maddox

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 123 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 111 citations into it; #22,809 of 35,345 by trust here, #11,327 by citations
• Probabilistic & Bayesian Methods — 54 citations into it; #1,240 of 1,525 by trust here, #658 by citations
• Optimization & Training — 46 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Scaling & Learning Theory — 30 citations into it
• Statistics and Probability — 27 citations into it
• Statistical and Nonlinear Physics — 15 citations into it
• Model Architectures — 10 citations into it

Whom Wesley J. Maddox trusts overall

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

1. David John Cameron MacKay
2. Radford M. Neal
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Kaiming He
6. Alex Krizhevsky
7. Yann LeCun
8. Yoshua Bengio
9. Max Welling
10. Karen Simonyan

Who trusts Wesley J. Maddox overall

1. Pavel Izmailov
2. Andrew Gordon Wilson
3. Erik A. Daxberger
4. Moloud Abdar
5. Nicholas Geneva
6. Jonathan Heek
7. Stanislav Fort
8. Samuel K. Ainsworth
9. Jean Kaddour
10. Zhiyun Lu

Papers from Wesley J. Maddox's perspective

Ordered by how strongly the researchers Wesley J. Maddox 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Simple Baseline for Bayesian Uncertainty in Deep Learning (2019) — 1,004 citations
• Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited (2020) — 68 citations
• Subspace Inference for Bayesian Deep Learning (2019) — 164 citations
• Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs (2018)