Jan A. Hiss

Molecular Biology · 2011–2016

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 38 citations into it; #5,432 of 17,773 by trust here, #5,914 by citations
• Computational Theory and Mathematics — 24 citations into it; #4,932 of 12,182 by trust here, #1,667 by citations
• Artificial Intelligence — 16 citations into it
• Immunology — 6 citations into it
• Organic Chemistry — 5 citations into it
• Spectroscopy — 5 citations into it

Whom Jan A. Hiss trusts overall

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

1. Gisbert Schneider
2. W. L. Jorgensen
3. Geoffrey E. Hinton
4. Roberto Todeschini
5. T. van Laarhoven
6. J. Urgen Schmidhuber
7. Yoshua Bengio
8. Paul J. Werbos
9. Radford M. Neal

Who trusts Jan A. Hiss overall

1. Garrett B. Goh
2. Charles Siegel
3. Abhinav Vishnu
4. Nathan Oken Hodas
5. M. Mahlapuu
6. Mengying Sun
7. Antonio Serrano
8. Maryam Bagherian
9. G. Handelman

Papers from Jan A. Hiss's perspective

Ordered by how strongly the researchers Jan A. Hiss 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
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. ImageNet Large Scale Visual Recognition Challenge
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Deep Learning in Drug Discovery (2016) — 644 citations
• Designing antimicrobial peptides: form follows function (2011) — 1,868 citations