Michael W. Dusenberry

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 95 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #29,190 of 35,345 by trust here, #12,244 by citations
• Computer Vision and Pattern Recognition — 54 citations into it
• Probabilistic & Bayesian Methods — 49 citations into it
• Optimization & Training — 21 citations into it
• Model Architectures — 18 citations into it
• Generative Models — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Michael W. Dusenberry trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Michael I. Jordan
7. Max Welling
8. Yann LeCun
9. Radford M. Neal
10. Jimmy Ba

Who trusts Michael W. Dusenberry overall

1. Dustin Tran
2. Danijar Hafner
3. Yeming Wen
4. Benjamin Kompa
5. Jasper Snoek
6. Pavel Izmailov
7. Jeremiah Zhe Liu
8. Jishnu Mukhoti
9. Moloud Abdar
10. Jimmy Ba

Papers from Michael W. Dusenberry's perspective

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

Their papers

• Analyzing the role of model uncertainty for electronic health records (2019) — 123 citations
• Measuring Calibration in Deep Learning (2019) — 692 citations
• Bayesian Layers: A Module for Neural Network Uncertainty (2018) — 150 citations
• Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors (2020) — 240 citations
• Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer (2019) — 301 citations
• SystemML: Declarative Machine Learning on Spark (2016) — 232 citations