Matthias Fey

Excited to announce Relational Foundation Models, moving supervised Relational Deep Learning into the in-context learning setting: ✅On-the-fly training-free predictions ✅Impressive out-of-the-box performance Paper: shorturl.at/M6ciN Try For Free: kumorfm.ai

🚀 Introducing KumoRFM — the world’s first Relational Foundation Model purpose-built for enterprise prediction tasks! KumoRFM reasons over complex relational data to deliver instant, accurate, in-context predictions — no task-specific model training required. A true game-changer…

Introducing the new Kumo platform: Build predictive and embedding models directly on your relational data — without feature engineering. Go from predictive modeling to predictive AI. hubs.ly/Q035xfgP0

🎉 Excited to announce the release of ContextGNN, our state-of-art and scalable recommendation model from Kumo! 🔗 ContextGNN paper: arxiv.org/abs/2411.19513 What makes ContextGNN unique? It combines pair-wise and two-tower representations into a single architecture, enabling…

💠 Stanford Graph Learning Workshop 2024! Join leaders from academia and industry to explore the latest in Machine Learning and AI. Topics include Relational domains, Foundation Models, Agents and more. Save the date: Tuesday, Nov 5, 2024, 09:00 - 18:00 PT. The event will be…

PyG 2.6 is here, including new models and examples on how to combine GNNs with LLMs! Thanks to many contributors who have made this release possible. Full release notes👇 github.com/pyg-team/pytor…

What an exciting week it has been at the #KDD conference in Barcelona! We were thrilled to be here and to have two of our very own, Hema Raghavan and Matthias Fey, speak on graph learning for enterprise and responsible graph neural networks.

🚀 Announcing RelBench: an open benchmark for deep learning on relational databases! RelBench is the foundational infrastructure for research in Relational Deep Learning (RDL), which brings modern AI to structured data. RelBench has databases, tasks, loaders, evaluators, and…

Check out our PyTorch Frame tech report arxiv.org/abs/2404.00776 We aim to push tabular deep learning to handle complex multi-modal, multi-tabular data, beyond conventional single-tabular data. We do so by seamlessly integrating PyTorch Frame with LLMs and Graph Neural Nets!

📢 We are hosting a webinar on March 6th 8am PT/5pm CET on the latest PyG release. The webinar covers everything you need to know about PyG 2.5, including a live demo on how set up and use our new distributed GNN training solution. Register for free 👇 eventbrite.com/e/webinar-pyg-…

🚀🎉 Excited to announce 🌟 PyTorch Frame 🌟 - our new open-source initiative in PyTorch! Dive into multi-modal tabular deep learning like never before! Link: github.com/pyg-team/pytor… #PyTorch #OpenSource (1/6)

Curious about what I’ve been working on over the last two years? Join me next Thursday for the launch of Kumo.AI’s groundbreaking declarative ML platform that will transform the world of predictive AI as we know it. 🚀 More at info.kumo.ai/revolutionizin… #AI…