Michael Robert DeWeese

Probabilistic & Bayesian Methods · 2008–2020

A researcher in the Science corpus: 5 papers; trusts 28 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 33 citations into it; #704 of 1,525 by trust here, #817 by citations
• Statistics and Probability — 23 citations into it; #9,875 of 12,932 by trust here, #9,714 by citations
• Cognitive Neuroscience — 17 citations into it; #4,428 of 10,852 by trust here, #5,990 by citations
• Artificial Intelligence — 16 citations into it; #11,794 of 35,345 by trust here, #17,110 by citations
• Statistical and Nonlinear Physics — 12 citations into it
• Electrical and Electronic Engineering — 8 citations into it
• Optimization & Training — 5 citations into it

Whom Michael Robert DeWeese trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Jascha Narain Sohl-Dickstein
4. Mark A. Girolami
5. Jesus A. Izaguirre
6. David John Cameron MacKay
7. Ziyun Wang
8. Toshiyuki TANAKA
9. Aapo Hyvärinen
10. Alexandros Beskos

Who trusts Michael Robert DeWeese overall

1. Jascha Narain Sohl-Dickstein
2. Daniel Lévy
3. Shuzo Sakata
4. C. Poo
5. Kenneth D. Harris
6. Artur Luczak
7. Yang Song
8. Benjamin Jackson Culpepper
9. Saeed Saremi

Papers from Michael Robert DeWeese's perspective

Ordered by how strongly the researchers Michael Robert DeWeese 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. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Minimum Probability Flow Learning (2009) — 72 citations
• A new method for parameter estimation in probabilistic models: Minimum probability flow (2020) — 63 citations
• Hamiltonian Monte Carlo Without Detailed Balance (2014) — 66 citations
• A Markov Jump Process for More Efficient Hamiltonian Monte Carlo (2015)
• Sparse Representation of Sounds in the Unanesthetized Auditory Cortex (2008) — 605 citations