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A geometric flow matching model for generative protein-ligand docking and affinity prediction. (ISMB 2025)
Paper: Modeling water and nitrogen dynamics in unsaturated soils using physics and multi-physics informed neural networks.
California-Delta estuary salinity estimation by PINN
Examples implementing physics-informed neural networks (PINN) in Pytorch
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
A framework for evaluating RAG pipelines, specifically adapted for the legal domain.
A Python tool for automatically extracting and analyzing review process timelines from academic journal articles (PDF format). This tool helps researchers understand the typical review duration of …
A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting
Official implementation for the "Plug-and-Play Linear Attention for Pre-Trained Image and Video Restoration Models" paper
This is a fork of SATA repo (CVPR 2025), which is invisiable.
[CVPR 2023] Official implementation for "CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion."
The existing blockchain-related academic papers. All papers are sorted according to the conference and published year. Welcome developers or researchers to add more published papers to this list.
Source codes for the paper "Using Machine Learning for Predicting Arbitrage Occurrences in Cryptocurrency Exchanges" which was accepted and presented at IEEE International Conference on Blockchain …
Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network
Official Implementation of TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting
这个仓库是由 Rebase 社区创建的，为区块链行业以及其他各行各业的企业和团队提供招聘信息披露机会，所有招聘信息都将在 Rebase 社区的所有媒体上进行发表。这都是免费的！
[IEEE BigData'24] Code for the paper "Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning".
[AAAI 2025] Official implementation of "xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition"