Kenneth Atz

Computational Theory and Mathematics · 2020–2023

A researcher in the Science corpus: 9 papers; trusts 64 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 191 citations into it; #9,627 of 12,182 by trust here, #3,562 by citations
• Materials Chemistry — 173 citations into it; #7,665 of 11,513 by trust here, #8,733 by citations
• Molecular Biology — 163 citations into it
• Computational Chemistry & Drug Discovery — 72 citations into it
• Artificial Intelligence — 65 citations into it
• Physical and Theoretical Chemistry — 31 citations into it
• Graph Learning — 30 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it

Whom Kenneth Atz trusts overall

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

1. Yann LeCun
2. Kristof T. Schütt
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Oliver T. Unke
8. J. Urgen Schmidhuber
9. Sepp Hochreiter
10. Ilya Sutskever

Who trusts Kenneth Atz overall

1. Shuo Zhang
2. Clemens Isert
3. Yang Liu

Papers from Kenneth Atz's perspective

Ordered by how strongly the researchers Kenneth Atz 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. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Combining generative artificial intelligence and on-chip synthesis for de novo drug design (2020) — 144 citations
• Leveraging molecular structure and bioactivity with chemical language models for de novo drug design (2023) — 142 citations
• Geometric deep learning on molecular representations (2021) — 410 citations
• QMugs, quantum mechanical properties of drug-like molecules (2022) — 164 citations
• Δ-Quantum machine-learning for medicinal chemistry (2021) — 37 citations
• Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (2023) — 59 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
• Machine Learning and Computational Chemistry for the Endocannabinoid System.