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Helpful tools and examples for working with flex-attention
Official repo for consistency models.
[ICLR23] First deep learning-based surrogate model that jointly learns the evolution model and optimizes computational cost via remeshing
Latent diffusion for generative precipitation nowcasting
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
Differentiable signal processing on the sphere for PyTorch
Graph Neural Network Library for PyTorch
DiffusionFastForward: a free course and experimental framework for diffusion-based generative models
PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
Python tools for climate and air quality model evaluation
Implementation of DeepMind's Deep Generative Model of Radar (DGMR) https://arxiv.org/abs/2104.00954
Techniques for deep learning with satellite & aerial imagery
Graph-based weather forecasting models. Originally, PyTorch implementation of Ryan Keisler's 2022 "Forecasting Global Weather with Graph Neural Networks" paper (https://arxiv.org/abs/2202.07575)
An open access book on scientific visualization using python and matplotlib
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Python package for easy access to weather and climate data
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Bootstrap Kubernetes the hard way. No scripts.
A curated list of resources on implicit neural representations.
[NeurIPS‘2021] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang
SwinIR: Image Restoration Using Swin Transformer (official repository)
A Python3 and PyTorch replication of Jean et al. (2016). Original paper Github: https://github.com/nealjean/predicting-poverty