ArchNet: A data hiding design for distributed machine learning systems

اسناد مشابه

• Designing Ontology-Based Knowledge Integration for Preprocessing of Medical Data in Enhancing a Machine Learning System for Coding Assignment of a Multi-Label Medical Text

  سند علمی۲۰۱۳Phanu Waraporn

  This paper discusses the designing of knowledge integration of clinical information extracted from distributed medical ontologies in order to ameliorate a machine learning-based multilabel coding assignment system. The proposed approach is implemented using a decision tree techni

• On The Importance of Execution Ordering in Graph-Based Distributed Machine Learning Systems

  سند علمی۲۰۱۸Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy Campbell

  Execution of operations in distributed machine learning systems has largely ignored dependencies between communication and computation ops. In this paper, we make the case that model-aware ordering of operations at individual machines can decrease the step time of training iterat

• MLbase: A Distributed Machine-learning System

  سند علمی۲۰۱۳CIDRTim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith

  Machine learning (ML) and statistical techniques are key to transforming big data into actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML algorithms is often overwhelming—many users do not understand the trade-offs and challenges of parame

• Communication Scheduling as a First-Class Citizen in Distributed Machine Learning Systems

  سند علمی۲۰۱۸Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy H. Campbell

  State-of-the-art machine learning systems rely on graphbased models, with the distributed training of these models being the norm in AI-powered production pipelines. The performance of these communication-heavy systems depends on the effective overlap of communication and computa

• Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model

  مقاله‌ی دسترسی آزاد۲۰۲۱Machine Learning['Giorgio Gnecco', 'Federico Nutarelli', 'Daniela Selvi', '']

  Abstract This work belongs to the strand of literature that combines machine learning, optimization, and econometrics. The aim is optimize data collection process in a specific statistical model, commonly used econometrics, employing an optimization criterion inspired by namely,

• A cone-beam X-ray computed tomography data collection designed for machine learning

  مقاله‌ی دسترسی آزاد۲۰۱۹Scientific DataHenriDer Sarkissian, FelixLucka, Maureenvan Eijnatten, GiuliaColacicco