We show communication schedulers' recent work proposed for ML collective...
Machine Learning (ML) models contain highly-parallel computations, such ...
With the growing model size, deep neural networks (DNN) are increasingly...
This work provides a theoretical analysis for optimally solving the pose...
This study presents a theoretical structure for the monocular pose estim...
Machine learning models made up of millions or billions of parameters ar...
Large ML models and datasets have necessitated the use of multi-GPU syst...
This work provides a theoretical framework for the pose estimation probl...
Recent trend towards increasing large machine learning models require bo...
Collective communication algorithms are an important component of distri...
Stochastic gradient descent (SGD) is an inherently sequential training
a...
Word embeddings capture semantic and syntactic similarities of words,
re...
Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes...
Stochastic gradient descent (SGD) is a well known method for regression ...