Pinned Repositories
accelerate-discoveries
Incorporating distribution of experts in order to better predict the future discovery of novel scientific connections
Adversarial-Examples
A practice project for implementing Adversarial Examples in Tensorflow
Adversarial_Examples_Papers
A list of recent papers about adversarial learning
AutoOED
AutoOED: Automated Optimal Experimental Design Platform
Awesome-Anything
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
awesome-camouflaged-object-detection
A curated list of awesome resources for camouflaged/concealed object detection (COD).
awesome-concealed-object-segmentation
awesome-image-inpainting-studies
A collection of awesome image inpainting studies.
bayesian-optimization-in-action
Source code for Bayesian Optimization in Action, published by Manning
Bgolearn
A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning
MichaelCSHN's Repositories
MichaelCSHN/awesome-concealed-object-segmentation
MichaelCSHN/awesome-image-inpainting-studies
A collection of awesome image inpainting studies.
MichaelCSHN/awesome-kan
A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold Network field.
MichaelCSHN/Awesome-Scientific-Language-Models
A Curated List of Language Models in Scientific Domains
MichaelCSHN/baybe
Bayesian Optimization and Design of Experiments
MichaelCSHN/CAMEVAL
[ICCV23] The Making and Breaking of Camouflage. Hala Lamdouar, Weidi Xie, Andrew Zisserman.
MichaelCSHN/chemcrow-public
Chemcrow
MichaelCSHN/Design-of-experiment-Python
Design-of-experiment (DOE) generator for science, engineering, and statistics
MichaelCSHN/foundry
Simplifying the discovery and usage of machine-learning ready datasets in materials science and chemistry
MichaelCSHN/generative-materials-discovery
Files and data needed to reproduce the machine learning results of the paper "Materials discovery using generative machine learning"
MichaelCSHN/gpt_academic
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。
MichaelCSHN/gptchem
MichaelCSHN/Grounded-Segment-Anything
Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
MichaelCSHN/HySUPP
An Open-Source Hyperspectral Unmixing Python Package
MichaelCSHN/IQA-PyTorch
👁️ 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, NIMA, DBCNN, WaDIQaM, BRISQUE, PI and more...
MichaelCSHN/LAKE-RED
[CVPR 2024] LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion.
MichaelCSHN/materials_discovery
MichaelCSHN/MatSci-LumEn
MatSci-LumEn: Materials Science Large Language Models Evaluation for text and data mining
MichaelCSHN/matsci-opt-benchmarks
A collection of benchmarking problems and datasets for testing the performance of advanced optimization algorithms in the field of materials science and chemistry.
MichaelCSHN/matsciml
Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
MichaelCSHN/matscipy
Materials science with Python at the atomic-scale
MichaelCSHN/MLMD
MLMD: a programming-free AI platform to predict and design materials
MichaelCSHN/polyVERSE
MichaelCSHN/PromptDataExtraction
Python module and scripts to run automated data extraction pipelines built using MaterialsBERT, GPT-3.5 and LlaMa 2 models.
MichaelCSHN/pygwalker
PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis
MichaelCSHN/recognize-anything
Open-source and strong foundation image recognition models.
MichaelCSHN/REINVENT4
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
MichaelCSHN/saliency
Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
MichaelCSHN/SARDet_100K
Offical implementation of MSFA and release of SARDet_100K dataset for Large-Scale Synthetic Aperture Radar (SAR) Object Detection
MichaelCSHN/torch-cam
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)