Nature

Distributed constrained combinatorial optimization leveraging hypergraph neural networks

Scalable addressing of high-dimensional constrained combinatorial optimization problems is a challenge that arises in several science and engineering disciplines. Recent work introduced novel ...
Nature

Reusability report: A distributed strategy for solving combinatorial optimization problems with hypergraph neural networks

Combinatorial optimization problems (COPs) encompass a class of problems that are aimed at finding optimal or near-optimal solutions within a finite solution space and that are prevalent in both ...
EurekAlert!

A fault tolerant framework for distributed training with negligible overhead

In the context of deep learning model training, checkpoint-based error recovery techniques are a simple and effective form of fault tolerance. By regularly saving the ...
Geeky Gadgets

Unsloth : The Secret Weapon for Faster Machine Learning Models

What if you could train massive machine learning models in half the time without compromising performance? For researchers and developers tackling the ever-growing complexity of AI, this isn’t just a ...
The Economist

Training AI models might not need enormous data centres

Once, the world’s richest men competed over yachts, jets and private islands. Now, the size-measuring contest of choice is clusters. Just 18 months ago, OpenAI trained GPT-4, its then state-of-the-art ...