D. Tischer

Molecular Biology · 2020–2023

A researcher in the Science corpus: 7 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 136 citations into it
• Computational Biology & Bioinformatics — 85 citations into it
• Materials Chemistry — 76 citations into it
• Generative Models — 30 citations into it
• Artificial Intelligence — 17 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it

Whom D. Tischer trusts overall

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

1. Diederik P. Kingma
2. Jonathan Ho
3. Ivan V. Anishchenko
4. Nicolas Chopin
5. Geoffrey E. Hinton
6. Tim Salimans
7. Jianyi Yang

Who trusts D. Tischer overall

1. Robin Pearce
2. Michael Jendrusch
3. Wengong Jin
4. Brian L. Trippe
5. Yang Zhang
6. Jan O. Korbel

Papers from D. Tischer's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Rethinking the Inception Architecture for Computer Vision
6. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Protein sequence design by conformational landscape optimization (2021) — 142 citations
• Deep learning methods for designing proteins scaffolding functional sites (2021) — 38 citations
• Robust deep learning based protein sequence design using ProteinMPNN (2022) — 1,912 citations
• Scaffolding protein functional sites using deep learning (2022) — 414 citations
• Design of proteins presenting discontinuous functional sites using deep learning (2020) — 40 citations
• Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022) — 347 citations
• De novo design of luciferases using deep learning (2023) — 377 citations