Abstract: Neural networks for speech separation generally exhibit high computational costs and large memory footprints. Moreover, typical separation networks have a fixed computational graph that ...

The Uncertainty-Aware Fourier Ptychography (UA-FP) framework marks a transformative milestone in computational imaging, revolutionizing the way we address system uncertainties. This innovative ...

Abstract: We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational ...

This work reviews CM-cfDNA methods applied to clinical oncology, emphasizing both machine learning (ML) techniques and mechanistic approaches. The latter integrate biological principles, enabling a ...

ABSTRACT: Graph burning is a model for describing the spread of influence in social networks and the generalized burning number b r ( G ) of graph G is a parameter to measure the speed of information ...

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge ...

OpenAI's recently unveiled o3 model is purportedly its most powerful AI yet, but with one big drawback: it costs ungodly sums of money to run, TechCrunch reports. Announced just over a week ago, o3 ...

No matter how elegant and clever the design is for a compute engine, the difficulty and cost of moving existing – and sometimes very old – code from the device it currently runs on to that new compute ...

I am training a video-llm model, where I encode log videos with a varying number of forward passes to avoid OOM issues. I would like to use ZeRO3, but using a part of the model a different number of ...

JD Vance has climbed to his current position as former President Donald Trump’s running mate, in part, by selling himself as a hillbilly, calling on his Appalachian background to bolster his ...