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Showing 1 to 20 of 33 for “"Distributed Machine Learning"”.
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Speeding Up Coded Distributed Machine Learning
… size often exceeds tens of terabytes. Meanwhile, machine learning has become the most important technique for big data analytics, and sophisticated models with thousands or even millions of parameters are designed to leverage big data. However, processing big data and training large models …
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Timed execution in distributed machine learning
Deep learning powers many transformative core technologies including Autonomous Driving, Natural Language Translation, and Automatic Medical Diagnosis. Its exceptional ability to extract intricate structures from high-dimensional data takes the credit for major advances in machine learning. …
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Distributed Machine Learning in Heterogeneous Edge Networks
… the network's edge. Meanwhile, the complexity of machine learning models has increased significantly, with state-of-the-art models for tasks like natural language processing and computer vision now containing billions of parameters.Distributed machine learning addresses the challenges posed by …
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Small-scale distributed machine learning in R
Machine learning is increasing in popularity, both in applied and theoretical statistical fields. Machine learning models generally require large amounts of data to train and thus are computationally expensive, both in the absolute sense of actual compute time, and in the relative sense of the …
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Efficient and Collaborative Methods for Distributed Machine Learning
… of modern devices has prompted a shift towards distributed systems that enable localized data storage and model training. While this evolution promises substantial potential, it introduces a series of challenges. Such challenges encompass addressing the heterogeneity across systems, data, …
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Toward communication-efficient and secure distributed machine learning
… recent years, there is an increasing interest in distributed machine learning. On one hand, distributed machine learning is motivated by assigning the training workload to multiple devices for acceleration and better throughput. On the other hand, there are machine-learning tasks requiring …
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Communication-centric cross-stack acceleration for distributed machine learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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Using distributed machine learning to predict arterial blood pressure
This thesis describes how to build a flow for machine learning on large volumes of data. The end result is EC-Flow, an end to end tool for using the EC-Star distributed machine learning system. The current problem is that analysing datasets on the order of hundreds of gigabytes requires overcoming …
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Learning generalizable device placement algorithms for distributed machine learning
We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be …
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Network Requirements for Distributed Machine Learning Training in the Cloud
… characterize the impact of network bandwidth on distributed machine learning training. I test four popular machine learning models (ResNet, DenseNet, VGG, and BERT) on an Nvidia A-100 cluster to determine the impact of bursty and non-bursty cross traffic (such as web-search traffic and long-lived …
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Distributed Machine Learning for Autonomous and Secure Cyber-physical Systems
… tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs. Towards achieving this overarching goal, this dissertation led to several major contributions. First, a comprehensive control and …
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SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning
… knowledge from big data. However, traditional machine learning approaches are not well fit to analyze the full value of big data. Explicitly, current research and practice of Machine learning do not fully support some important features for big data analytics such as incremental learning, …
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Toward efficient online scheduling for large-scale distributed machine learning system
<p>Thanks to the rise of machine learning (ML) and its vast applications, recent years have witnessed a rapid growth of large-scale distributed ML frameworks, which exploit the massive parallelism of computing clusters to expedite ML training jobs. However, the proliferation of large-scale …
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A framework for privacy-preserving, distributed machine learning using gradient obfuscation
Made available in DSpace on 2018-03-02T19:59:44Z (GMT). No. of bitstreams: 2 PHADKE-THESIS-2017.pdf: 14510299 bytes, checksum: e357657487b88e1a0ee47863e6b7f0bc (MD5) LICENSE.txt: 4210 bytes, checksum: fc35d28054217b84ccd68356449c9d17 (MD5) Previous issue date: 2017-07-18
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Software-Hardware Optimizations for Efficient Collective Communications in Distributed Machine Learning Platforms
Foundation machine learning (ML) models have emerged as one of the most prominent applications in modern computing, exemplified by mixture-of-experts–based large language models. The immense resource demands of these models have driven the development of large-scale, high-performance computing …
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FlexGP 2.0 : multiple levels of parallelism in distributed machine learning via genetic programming
This thesis presents FlexGP 2.0, a distributed cloud-backed machine learning system. FlexGP 2.0 features multiple levels of parallelism which provide a significant improvement in accuracy v.s. elapsed time. The amount of computational resources in FlexGP 2.0 can be scaled along several dimensions …
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TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Machine Learning Training Jobs
… system, called TopoOpt, co-optimizes the distributed training process across three dimensions: computation, communication, and network topology. TopoOpt uses a novel alternating optimization technique and a group theory-inspired algorithm to find the best network topology and routing plan, …
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Topology-aware distributed graph processing for tightly-coupled clusters
… Specifically, we look at a class of distributed machine learning systems called distributed graph processing systems, and run them on NCSA Blue Waters. Partitioning the graph is key to achieving performance in distributed graph processing systems. We present new topology-aware …
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Scalable Embedded Tiny Machine Learning (SETML): A General Framework for Embedded Distributed Inference
The growth of machine learning applications has increased the necessity of lightweight, energyefficient solutions for resource-constrained devices such as the STM32C011F6 microcontroller. However, such devices struggle with supporting larger models even after miniaturization techniques such as …
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Performance analysis of machine learning applications on rapid: a highly parallel computer architecture
… past few years, the interest and application of machine learning algorithms has risen exponentially. Machine learning has found extensive use in diverse fields like self-driving cars, speech recognition, image processing, computer vision, molecular biology, security etc. A lot of recent research …
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