John Wilmes

Artificial Intelligence · 2011–2018

A researcher in the Science corpus: 4 papers; trusts 31 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #20,663 of 35,345 by trust here, #21,316 by citations
• Statistical Learning Theory — 24 citations into it; #874 of 1,038 by trust here, #948 by citations
• Model Architectures — 17 citations into it
• Optimization & Training — 15 citations into it
• Scaling & Learning Theory — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Algorithms & Complexity — 6 citations into it; #423 of 516 by trust here, #435 by citations

Whom John Wilmes trusts overall

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

1. Francis R. Bach
2. Yuanzhi Li
3. Moritz Hardt
4. Michael Kearns
5. Sanjeev Arora
6. Amit Daniely
7. Tengyu Ma
8. Andrew R. Barron
9. B. Recht
10. Rong Ge

Who trusts John Wilmes overall

1. Yuanzhi Li
2. Zeyuan Allen-Zhu
3. Santosh Srinivas Vempala
4. Ronen Basri
5. Surbhi Goel

Papers from John Wilmes's perspective

Ordered by how strongly the researchers John Wilmes 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds (2018) — 51 citations
• Polynomial Convergence of Gradient Descent for Training One-Hidden-Layer Neural Networks (2018) — 21 citations
• On the Complexity of Learning Neural Networks (2017) — 63 citations
• PRIMER FOR THE ALGEBRAIC GEOMETRY OF SANDPILES (2011) — 68 citations