Hongming Chen

Computational Theory and Mathematics · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 47 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 117 citations into it; #12,109 of 12,182 by trust here, #845 by citations
• Molecular Biology — 91 citations into it; #17,549 of 17,773 by trust here, #4,912 by citations
• Materials Chemistry — 72 citations into it; #1,422 of 11,513 by trust here, #3,055 by citations
• Computational Chemistry & Drug Discovery — 66 citations into it; #129 of 425 by trust here, #163 by citations
• Artificial Intelligence — 62 citations into it; #11,954 of 35,345 by trust here, #8,810 by citations
• Computer Vision and Pattern Recognition — 49 citations into it
• Generative Models — 37 citations into it
• Model Architectures — 26 citations into it

Whom Hongming Chen trusts overall

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

1. Sepp Hochreiter
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Yann LeCun
7. George A. Miller
8. Marwin H. S. Segler
9. Stuart E. Dreyfus
10. Esben Jannik Bjerrum

Who trusts Hongming Chen overall

1. Tânia F Cova
2. Kit-Kay Mak
3. Sergey I. Nikolenko
4. Clémence Réda
5. Rohan Gupta
6. Mengying Sun
7. Antonio Serrano
8. Nathan Brown
9. Andrew F. Zahrt
10. Jiaxuan You

Papers from Hongming Chen's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Molecular de-novo design through deep reinforcement learning (2017) — 1,267 citations
• Application of Generative Autoencoder in De Novo Molecular Design (2017) — 375 citations
• The rise of deep learning in drug discovery. (2018) — 1,560 citations
• A de novo molecular generation method using latent vector based generative adversarial network (2019) — 356 citations
• Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction (2022) — 65 citations
• Randomized SMILES strings improve the quality of molecular generative models (2019) — 340 citations