GraphML News

It’s Friday - time to look back at what happened in the field this week.

📚 Blogs & Books

(Editors’ Choice 👍) An Introduction to Poisson Flow Generative Models by Ryan O’Connor. Diffusion models are the hottest topic in Geometric Deep Learning but have an important drawback - the sampling is slow 🐌 due to necessity of performing 100-1000 forward passes. Poisson Flow generative models take inspiration from physics and offer another look at the generation process that allows much much faster sampling. This blog gives a very detailed and pictorial explanation of Poisson Flows.

Awesome GFlowNets by Narsil-Dinghuai Zhang. Generative Flow Networks (GFlowNets) bring together generative modeling with ideas from reinforcement learning and show especially promising results in drug discovery. This Awesome repo will get you acquainted with the main ideas, most important papers, and some implementations

Sheaf Theory through Examples - a book by Daniel Rosiak on the sheaf theory. If you felt you want to know more after reading the Sheaf Diffusion paper - this would be your next step.

🗞️ News & Press

Elon Musk finally acquired Twitter so it’s time to move to Telegram

Mila and Helmholtz Institute announced a new German-Canadian partnership on developing causal models of the cell. As Geometric DL is in the heart of modern structural biology, we’ll keep an eye on the future outcomes.

🛠️ Code & Data

We somehow missed that but catching up now - the DGL team at Amazon published the materials of the KDD’2022 tutorial on GNNs in Life Sciences.

Geometric Kernels - a new fresh framework for kernels and Gaussian processes on non-Euclidean spaces (including graphs, meshes, and Riemannian manifodls). Supports PyTorch, TensorFlow, JAX, and Numpy.

A post with the hot new papers for your weekend reading will be arriving shortly!