AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
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Updated
Oct 20, 2022 - Python
AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)
A curated list of awesome AI and Bioinformatics.
Este proyecto es una aplicación web que utiliza la API de OpenAI para generar automáticamente presentaciones educativas. La aplicación permite a los usuarios ingresar información sobre el tema de la clase, la duración, el nivel educativo y las palabras clave relevantes.
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
Must-read papers on NLP for science.
GPT (Generative Pre-trained Transformer) for de novo molecular design by enforcing specified targets
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. KG4SL is the first graph neural network (GNN)-based model that uses knowledge graph for SL prediction.
Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis
[NeurIPS 2022] The implementation for the paper "Equivariant Graph Hierarchy-Based Neural Networks".
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model (NeurIPS 2023 Poster)
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
「機械学習による分子最適化」のサポートページ
ChatCell: Facilitating Single-Cell Analysis with Natural Language
The official implementation of LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion (NeurIPS 2023 Spotlight)
List of Geometric GNNs for 3D atomic systems
The official code for "TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation"
[ICLR'24] "DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training" by Aochuan Chen*, Yimeng Zhang*, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu
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