Luca Pasquali

Molecular Biology · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 31 citations into it; #5,288 of 17,773 by trust here, #8,791 by citations
• Artificial Intelligence — 20 citations into it
• Statistical and Nonlinear Physics — 19 citations into it; #3,657 of 10,127 by trust here, #2,370 by citations
• Materials Chemistry — 15 citations into it; #3,097 of 11,513 by trust here, #7,093 by citations
• Spectroscopy — 12 citations into it
• Atomic and Molecular Physics, and Optics — 8 citations into it
• Computational Mechanics — 8 citations into it
• Model Architectures — 8 citations into it

Whom Luca Pasquali trusts overall

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

1. Frank No'e
2. Igor Mezi'c
3. Hao Wu
4. Gregory R. Bowman
5. Diederik P. Kingma
6. Geoffrey E. Hinton
7. Vinod Nair
8. Christof Schutte
9. Vijay S. Pande
10. Yann LeCun

Who trusts Luca Pasquali overall

1. Frank No'e
2. Hao Wu
3. Jared D. Willard
4. Steven L. Brunton
5. Naoya Takeishi
6. Seyed Mohamad Moosavi
7. Kathleen P. Champion
8. Alexandre Tkatchenko
9. Xiaowei Jia

Papers from Luca Pasquali's perspective

Ordered by how strongly the researchers Luca Pasquali 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. Gradient-based learning applied to document recognition
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. ImageNet classification with deep convolutional neural networks
6. Rectified Linear Units Improve Restricted Boltzmann Machines
7. Long Short-Term Memory
8. Human-level control through deep reinforcement learning
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• VAMPnets for deep learning of molecular kinetics (2017) — 682 citations
• Deep Generative Markov State Models (2018) — 68 citations