Stars
Collection of awesome medical dataset resources.
Must-read papers on graph foundation models (GFMs)
Image registration using discrete Fourier transform.
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 curated list of awesome responsible machine learning resources.
DeepWMH: Annotation-free white matter hyperintensity (WMH) lesion segmentation tool using deep learning.
Friends don't let friends make certain types of data visualization - What are they and why are they bad.
Xournal++ is a handwriting notetaking software with PDF annotation support. Written in C++ with GTK3, supporting Linux (e.g. Ubuntu, Debian, Arch, SUSE), macOS and Windows 10. Supports pen input fr…
[MedIA Best Paper Award] Official implementation of MedIA paper "BayeSeg: Bayesian Modelling for Medical Image Segmentation with Interpretable Generalizability"
Lightweight jekyll theme for your CV with dark mode support
ChatGPT Implementation for Twitch Streamers
Reproducible and Programmatic Human Neuroimaging Visualisations
A playbook for systematically maximizing the performance of deep learning models.
Compilation of high-profile real-world examples of failed machine learning projects
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Numpy implementation of Hilbert curves in arbitrary dimensions
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.
TeXstudio is a fully featured LaTeX editor. Our goal is to make writing LaTeX documents as easy and comfortable as possible.
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
TensorFlow 2 library implementing Graph Neural Networks
Code and resources on scalable and efficient Graph Neural Networks (TNNLS 2023)