Xingang Peng
Molecular Biology · 2022–2023
A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 62 citations into it
- Materials Chemistry — 55 citations into it
- Computational Theory and Mathematics — 50 citations into it
- Generative Models — 31 citations into it
- Artificial Intelligence — 25 citations into it
- Computational Chemistry & Drug Discovery — 23 citations into it
- Computational Biology & Bioinformatics — 18 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it
Whom Xingang Peng trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kristof T. Schütt
- Max Welling
- Diederik P. Kingma
- Geoffrey E. Hinton
- Dávid Bajusz
- Taco Cohen
- Yoshua Bengio
- Alex Krizhevsky
- Ashish Vaswani
- Shitong Luo
Who trusts Xingang Peng overall
Papers from Xingang Peng's perspective
Ordered by how strongly the researchers Xingang Peng trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- GENERATIVE ADVERSARIAL NETS
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets (2022) — 333 citations
- Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022) — 305 citations
- 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023) — 326 citations