GitHub

TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling" (ICLR 2025)

This is the official repository of the paper "TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling". It consists of two parts: One dot represents a performance score on one ...
Microsoft

Rethinking Machine Learning Collective Communication as a Multi Commodity Flow problem

Cloud operators utilize collective communication optimizers to enhance the efficiency of the single-tenant, centrally managed training clusters they manage. However, current optimizers struggle to ...
IEEE

Improving Floating Offshore Wind Farm Flow Control With Scalable Model-Based Deep Reinforcement Learning

Abstract: This paper proposes a model-based deep reinforcement learning (DRL) framework to maximize the total power output and minimize the fatigue load of a floating offshore wind farm subject to ...
Opinion

Context Monsters And Dropout Layers: The Job Market As A Neural Net

Entry jobs are inputs, and middle managers are "dropout layers." See why the few remaining executives are surging.
Sports Illustrated

Browns dig deep on depth chart for solutions to recent offensive line injuries

The Cleveland Browns are having to find some hidden gems on the bench with the latest injuries to the offensive line affecting the lineup. If the Cleveland Browns already didn't have enough problems ...
IEEE

A Deep Learning-Based Approach for Two-Phase Flow Pattern Classification Using Void Fraction Time Series Analysis

Abstract: Flow regime classification is essential for analyzing and modeling two-phase flows, as it demarcates the flow behavior and influences the selection of appropriate predictive models. Machine ...