Clemens Isert

Computational Theory and Mathematics · 2021–2023

A researcher in the Science corpus: 5 papers; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 85 citations into it
• Molecular Biology — 83 citations into it
• Materials Chemistry — 79 citations into it
• Atomic and Molecular Physics, and Optics — 26 citations into it
• Physical and Theoretical Chemistry — 22 citations into it
• Spectroscopy — 11 citations into it
• Catalysis — 6 citations into it

Whom Clemens Isert trusts overall

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

1. Stefan Grimme
2. Florian Weigend
3. Jeng-Da Chai
4. Kristof T. Schütt
5. Sereina Riniker
6. R. Ramakrishnan
7. Christoph Bannwarth
8. Felix A. Faber
9. Gisbert Schneider
10. Kenneth Atz

Who trusts Clemens Isert overall

1. Kenneth Atz

Papers from Clemens Isert's perspective

Ordered by how strongly the researchers Clemens Isert 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. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• QMugs, quantum mechanical properties of drug-like molecules (2022) — 164 citations
• Δ-Quantum machine-learning for medicinal chemistry (2021) — 37 citations
• Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity (2023) — 35 citations
• Translating from Proteins to Ribonucleic Acids for Ligand‐binding Site Detection (2022) — 6 citations
• Structure-based drug design with geometric deep learning (2022) — 178 citations