About Code release for “RoPINN: Region Optimized Physics-Informed Neural Networks” (NeurIPS 2024), https://arxiv.org/abs/2405.14369

Implementation of Switch Transformers from the paper: "Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity"

Implementation of ST-Moe, the latest incarnation of MoE after years of research at Brain, in Pytorch

The Pytorch implementation for "Uncertainty-guided Model Generalization to Unseen Domains" (CVPR 2021)

PyTorch Implementation of StyleSinger(AAAI 2024): Style Transfer for Out-of-Domain Singing Voice Synthesis

Paper Summary for Relations between Trustworthy AI Concepts

Official implementation of the paper "From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching"

[ArXiv' 24] U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

Official code for "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"

[NeurIPS 2024] BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models

Kolmogorov Arnold Network (KAN) for Time Series Forecasting (TSF)

第三届阿里云磐久智维算法大赛 - 基于大规模日志的故障诊断

Physics-informed neural networks package

Source codes for UDEL.

A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…

A simple feature-based time series classifier using Kolmogorov–Arnold Networks

Kolmogorov Arnold Network (KAN) for Time Series Forecasting (TSF)

For the propose of showing the calibration process of the Bayesian inference in gravitational wave astronomy

Official Code for "CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting"

[ICML 2024] A novel, efficient approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks

Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.

Rust 程序设计语言（2021 edition）

A playbook for systematically maximizing the performance of deep learning models.

Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

[AAAI 2024] PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion