lujiajin Goto Github PK

followers: 55.0 following: 28.0 repos: 23.0 gists: 0.0

Name: Lujia Jin

Type: User

Company: Peking University

Bio: Ph.D. candidate at Peking University. Research: deep-learning, image denoising, image super-resolution, and medical image processing. Life: photography.

Location: Yiheyuan Road #5, Haidian District, Beijing

Lujia Jin's Projects

autoxhs

📛 Autoxsh is an open-source tool that utilizes OpenAI's API to automate the generation and publishing of content on Xiaohongshu (Little Red Book), including images, titles, text, and tags.

awesome-graph-self-supervised-learning

Awesome Graph Self-Supervised Learning

awesome-protein-representation-learning

Awesome Protein Representation Learning

awesome-segment-anything

Tracking and collecting papers/projects/others related to Segment Anything.

basis-prediction-networks-pytorch

Reimplementation of the paper "Basis Prediction Networks for Effective Burst Denoising with Large Kernels" by using PyTorch.

codefuse-modelcache

A LLM semantic caching system aiming to enhance user experience by reducing response time via cached query-result pairs.

colormap_matlab

Quick transfer packages from Python matplotlib to MATLAB

cs-base

图解计算机网络、操作系统、计算机组成、数据库，共 1000 张图 + 50 万字，破除晦涩难懂的计算机基础知识，让天下没有难懂的八股文！🚀 在线阅读：https://xiaolincoding.com

fast-dit

Improved PyTorch Implementation of "Scalable Diffusion Models with Transformers"

hyperparameter-optimization-of-machine-learning-algorithms

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

kdga

Code for NeurIPS 2022 paper "Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks"

kernel-prediction-networks-pytorch

Reimplement of 'Burst Denoising with Kernel Prediction Networks' and 'Multi-Kernel Prediction Networks for Denoising of Image Burst' by using PyTorch

mape-ppi

Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"