Michael Weisz

Statistics and Probability · 2017

A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 11 citations into it
• Artificial Intelligence — 10 citations into it; #17,851 of 35,345 by trust here, #25,040 by citations
• Causal Inference & Discovery — 5 citations into it; #202 of 364 by trust here, #288 by citations
• Management Science and Operations Research — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Michael Weisz trusts overall

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

1. Donald B. Rubin
2. Yanbo Xu
3. Jennifer L. Hill
4. Diederik P. Kingma
5. Paul R. Rosenbaum
6. Fredrik D. Johansson
7. Uri Shalit
8. Stefan Wager
9. Yarin Gal
10. Susan Athey

Who trusts Michael Weisz overall

1. Ahmed M. Alaa
2. Individualized Treat
3. Ioana Bica
4. Mihaela van der Schaar

Papers from Michael Weisz's perspective

Ordered by how strongly the researchers Michael Weisz trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Dropout: a simple way to prevent neural networks from overfitting
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Deep Counterfactual Networks with Propensity-Dropout (2017) — 94 citations