Uncertainty-aware representation learning (URL) benchmark

We introduce a novel approach for parameter generation, named neural network parameter diffusion (p-diff), which employs a standard latent diffusion model to synthesize a new set of parameters

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Visualization tool for ReLU NNs trained on 2D data

Homemade GP codes for easy DIY and study

Segment Anything in Medical Images

Deep convolutional gaussian processes.

Implementation of Toolformer, Language Models That Can Use Tools, by MetaAI

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

An Obsidian plugin for day planning with a clean UI and a simple task format

Medical image registration using deep learning

Medical imaging toolkit for deep learning

An Introduction to Transparent Machine Learning

A highly efficient implementation of Gaussian Processes in PyTorch

To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of …

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

A framework for data augmentation for 2D and 3D image classification and segmentation

MindMaker UE4 Machine Learning Toolkit

Task management for the Obsidian knowledge base.

SAM: Sharpness-Aware Minimization (PyTorch)

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!

AI Toolkit for Healthcare Imaging

Version 2 of the minc-toolkit, uses tools based on ITK version 4.x