Mitch Hill

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 552 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #4,917 of 21,575 by trust here, #6,737 by citations
• Artificial Intelligence — 98 citations into it; #6,369 of 35,345 by trust here, #9,369 by citations
• Generative Models — 79 citations into it
• Probabilistic & Bayesian Methods — 54 citations into it; #534 of 1,525 by trust here, #521 by citations
• Optimization & Training — 23 citations into it
• Safety, Robustness & Interpretability — 20 citations into it
• Statistical and Nonlinear Physics — 20 citations into it
• Signal Processing — 17 citations into it

Whom Mitch Hill trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Song-Chun Zhu
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. Radford M. Neal
9. Ying Nian Wu
10. Jianwen Xie

Who trusts Mitch Hill overall

1. Erik Nijkamp
2. Yilun Du
3. Yang Song
4. Tian Han
5. Zhisheng Xiao
6. Song-Chun Zhu
7. Will Grathwohl
8. K. J. Joseph
9. Bo Pang
10. Weili Nie

Papers from Mitch Hill's perspective

Ordered by how strongly the researchers Mitch Hill 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model (2019) — 236 citations
• On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models (2019) — 185 citations
• Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model (2018) — 74 citations
• Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models (2020) — 97 citations
• Building a Telescope to Look Into High-Dimensional Image Spaces (2018) — 10 citations