Results 101 to 110 of about 120,084 (296)
Abstracting Systems Challenges from Distributed Deep Learning
, 2021 State-of-the-art distributed deep learning systems, such as TensorFlow and PyTorch, are built on rigid assumptions that tightly couple model training and inference with the underlying hardware.
Or, Andrew
core
, 2023 Continuously increasing data volumes from multiple sources, such as simulation and experimental measurements, demand efficient algorithms for an analysis within a realistic timeframe.
Morris Riedel +9 more
core +1 more source
Entropy-Guided Hierarchical Scheduling for Elastic Distributed Deep Learning
Applied SciencesShared GPU clusters often execute multiple distributed training jobs concurrently under fluctuating contention. We reinterpret this setting as a two-scale control problem, where the micro scale captures intra-job learning dynamics and the macro scale ...
Teh-Jen Sun, Eui-Nam Huh
doaj +1 more source
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)
IEEE Access, 2019 Distributed learning across coalitions is becoming popular for multi-centric implementation of deep learning models. However, the level of trust between the members of a coalition can vary and requires different security architectures.
Sebastien Lugan +5 more
doaj +1 more source
Advancing Age Modulates Associations Between Cognitive Impairment and Brain Volumes in Early MS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Introduction Cognitive impairment is common in multiple sclerosis (MS), but manifestations following the first demyelinating event are relatively unexplored. We investigated cross‐sectional associations between magnetic resonance imaging (MRI)–derived brain volumes and the presence of cognitive impairment outcomes five years after the first ...
Piriyankan Ananthavarathan +14 more
wiley +1 more source
A Systematic Review of Distributed Deep Learning Frameworks for Big Data
, 2022 Traditional Machine Learning and Deep Learning techniques (data acquisition, preparation, model training and evaluation) take a lot of computational resources and time to produce even a simple prediction model, especially when implemented on a single ...
Simona Colucci +2 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou +16 more
wiley +1 more source
Privacy-Preserving Distributed Deep Learning via Homomorphic Re-Encryption
, 2019 The flourishing deep learning on distributed training datasets arouses worry about data privacy. The recent work related to privacy-preserving distributed deep learning is based on the assumption that the server and any learning participant do not ...
Fengyi Tang +4 more
core +1 more source
IEEE AccessThis paper studies a deep reinforcement learning technique for distributed resource allocation among cognitive radios operating under an underlay dynamic spectrum access paradigm which does not require coordination between agents during learning. The key
Ankita Tondwalkar, Andres Kwasinski
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Digital technologies hold promise for transforming healthcare by enhancing personalized treatments and offer valuable opportunities to improve patient care. Here, we evaluated several novel, self‐administered, home‐based, digital endpoints for their association with corresponding conventional standard clinical measures (primary) in ...
Arne Mueller +14 more
wiley +1 more source