2022 comes to an end and it is about time to sit down and reflect upon the achievements made in Graph ML as well as to hypothesize about possible breakthroughs in 2023. Link

In this tutorial, you’ll learn how to deploy diffusion models at scale and build a text-to-image generator Link Muse

We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs. Link

We believe that by doing this we will create a revolution in innovation in language. In the same way that stable-diffusion helped the world make art and images in new ways we hope Open Assistant can help improve the world by improving language itself. Link

The Rich API makes it easy to add color and style to terminal output. Rich can also render pretty tables, progress bars, markdown, syntax highlighted source code, tracebacks, and more — out of the box. Link

Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms. The CLRS Algorithmic Reasoning Benchmark (CLRS) consolidates and extends previous work toward evaluation algorithmic reasoning by providing a suite of implementations of classical algorithms. These algorithms have been selected from the third edition of the standard Introduction to Algorithms by Cormen, Leiserson, Rivest and Stein. Link

We are excited to present this year’s picks for the most innovative developments in the Python ecosystem. From this edition, we are expanding our list to include not only libraries per-se, but also tools that are built to belong in the Python ecosystem — some of which are not written in Python as you’ll see. Link

Torchview provides visualization of pytorch models in the form of visual graphs. Visualization includes tensors, modules, torch.functions and info such as input/output shapes. Link

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback. Link Link Link Link Link

This is the official repository of DeepMTP, a deep learning framework that can be used with multi-target prediction (MTP) problems. MTP can be seen as an umbrella term that cover many subareas of machine learning, which include multi-label classification (MLC), multivariate regression (MTR), multi-task learning (MTL), dyadic prediction (DP), and matrix completion (MC). The implementation is mainly written in Python and uses Pytorch for the implementation of the neural network. The goal is for any user to be able to train a model using only a few lines of code.

A list of works and resources about double category theory, with a particular focus on applications. (If you’d like to add more, edit this nLab page) Link

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Category theory is a way of thinking and structuring one’s knowledge grounded in the idea of compositionality. Originating in abstract mathematics, this is a formal language that has since spread to numerous fields, becoming a topic of interest for a growing number of researchers. It’s helped build rigorous bridges between seemingly disparate scientific areas, showing great potential as a cohesive force in the scientific world. These fields include physics, chemistry, computer science, game theory, systems theory, database theory, and most importantly for us, machine learning, where it’s seen a steady growth.

Dr. Petar Veličković is a Staff Research Scientist at DeepMind, he has firmly established himself as one of the most significant up and coming researchers in the deep learning space. He invented Graph Attention Networks in 2017 and has been a leading light in the field ever since pioneering research in Graph Neural Networks, Geometric Deep Learning and also Neural Algorithmic reasoning. If you haven’t already, you should check out our video on the Geometric Deep learning blueprint, featuring Petar.

Category Theory has been finding increasing applications in machine learning. This repository aims to list all of the relevant papers, grouped by fields. Link