Stars
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
Pathology Foundation Model - Nature Medicine
PyTorch-based toolkit for landmark localization
Code related to the paper "Reliable uncertainty quantification for 2D/3D automatic anatomical landmark localization using multi-output conformal prediction" by Jonkers et al. (2025)
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
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 processing for AI applications.
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
Task management for the Obsidian knowledge base.
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.