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The University of New South Wales
- Sydney, Australia
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Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Long list of geospatial tools and resources
Techniques for deep learning with satellite & aerial imagery
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
A collection of resources and papers on Diffusion Models
A summary of related works about flow matching, stochastic interpolants
A curated list of Earth Science's Artificial Intelligence (AI) tutorials, notebooks, software, datasets, courses, books, video lectures and papers. Contributions most welcome.
A curated list of Decision Transformer resources (continually updated)
A curated list of awesome model based RL resources (continually updated)
A curated list of Multi-Modal Reinforcement Learning resources (continually updated)
A curated list of Diffusion Model in RL resources (continually updated)
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
This repository contains code to reproduce the experiments in the preprint "MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning"
An extension of LightGBM to probabilistic modelling
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
A Python package for tackling diverse environmental prediction tasks with NPs.
Code associated with the paper 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'
Pytorch implementation of Diffusion Models (https://arxiv.org/pdf/2006.11239.pdf)
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
IEEE ICMLA 2019 Data Science Tutorial - using data to answer questions
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Apps Script library for synchronising Google Drive folder with Remarkable reader.
Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019