Jaechang Lim
Materials Chemistry · 2018–2020
A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 32 citations into it
- Artificial Intelligence — 14 citations into it
- Generative Models — 9 citations into it
Whom Jaechang Lim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Sepp Hochreiter
- Marcus Olivecrona
- Thomas Blaschke
- Max Welling
- J. Urgen Schmidhuber
- Geoffrey E. Hinton
- Gabriel Lima Guimaraes
- John J. Irwin
- Artur Kadurin
Who trusts Jaechang Lim overall
- Tomohide Masuda
- Nathan Brown
- Xiao Wang
- Hannes Stärk
- Matthew Ragoza
- Petar Velickovic
- Marco Fiscato
- Sean T. Flannery
Papers from Jaechang Lim's perspective
Ordered by how strongly the researchers Jaechang Lim trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation (2019) — 366 citations
- Molecular generative model based on conditional variational autoencoder for de novo molecular design (2018) — 408 citations
- PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions (2020) — 149 citations