Popular GitHub Repositories for Partial Property

Learning in infinite dimension with neural operators.

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

Message Passing Neural Networks for Molecule Property Prediction

Scaled-YOLOv4: Scaling Cross Stage Partial Network

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Unofficial implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions". Try at: www.fixmyphoto.ai

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' [Liu+, ECCV2018]

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

[ICCV 2023] You Only Look at One Partial Sequence

This is a ChatGPT based prompt generation model for MidJorney. The purpose of this model is to simplify the creation of images and increase their creativity. By introducing a partial hint, ChatGPT creates a follow-up that can be used to stimulate creativity and provide new ideas.

Graph Neural PDEs

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

18.S096 - Applications of Scientific Machine Learning

Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.