Unifying Graph Machine Learning with a Fresh Approach
A novel diffusion framework looks to simplify graph machine learning tasks across industries, potentially altering financial forecasting and network optimization.
Read the full articleYou might also wanna read
New Research at ICML 2026: Graph Models and Diffusion
Key works in graph foundation models and diffusion transformer optimization were presented at the ICML 2026 conference. The industry is shif
Mathematical Unification of Decision Trees and Diffusion Models via Global Trajectory Score Matching
Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic.
Exploring Time Series Forecasting on Graph Structured Entities
Time series forecasting is a cornerstone in modern business analytics, whether it is concerned with anticipating market trends, user behavio
Research Shows Diffusion Models Outperform Autoregressive Models in Data-Constrained AI Settings
Check out our new blog post on "Diffusion beats Autoregressive in Data-Constrained settings". The era of infinite internet data is ending. T
Core Principles and Mathematical Foundations of Diffusion Models
This monograph presents the core principles that have guided the development of diffusion models, tracing their origins and showing how dive
Google's DiffusionGemma achieves 4x faster text generation using diffusion-based approach
An overview of DiffusionGemma, an exceptionally fast text generation model with up to 4x faster speeds.

Comments
Sign in to join the conversation.
No comments yet. Be the first.