David A. Winkler

Materials Chemistry · 2000–2022

A researcher in the Science corpus: 6 papers; trusts 23 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 153 citations into it; #6,432 of 11,513 by trust here, #9,927 by citations
• Computational Theory and Mathematics — 110 citations into it; #8,901 of 12,182 by trust here, #3,339 by citations
• Computational Chemistry & Drug Discovery — 49 citations into it
• Artificial Intelligence — 31 citations into it; #25,189 of 35,345 by trust here, #30,347 by citations
• Electrical and Electronic Engineering — 23 citations into it
• Spectroscopy — 21 citations into it
• Biomedical Engineering — 16 citations into it
• Model Architectures — 15 citations into it

Whom David A. Winkler trusts overall

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

1. David John Cameron MacKay
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Igor V. Tetko
5. Yann LeCun
6. Kunihiko Fukushima
7. Mário A. T. Figueiredo
8. Gary G. Yen
9. David E. Rumelhart
10. Radford M. Neal

Who trusts David A. Winkler overall

1. Igor I. Baskin
2. C. Onyutha
3. Alexandru Korotcov

Papers from David A. Winkler's perspective

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

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

Their papers

• A renaissance of neural networks in drug discovery (2016) — 213 citations
• QSAR without borders (2020) — 546 citations
• Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models (2015) — 696 citations
• Bayesian Regularization of Neural Networks (2009) — 633 citations
• Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. (2022) — 409 citations
• Robust QSAR Models from Novel Descriptors and Bayesian Regularised Neural Networks (2000) — 59 citations