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Showing 1 to 8 of 8 for “"distributed deep learning"”.
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Towards a Resource Efficient Framework for Distributed Deep Learning Applications
Distributed deep learning has achieved tremendous success for solving scientific problems in research and discovery over the past years. Deep learning training is quite challenging because it requires training on large-scale massive dataset, especially with graphics processing units (GPUs) in …
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Distributed Deep Learning in IoT: Splitting Neural Networks in Inference and Training
… Knowledge Distillation via Collaboratively Learning (KDCL) to Split Learning to get a distilled model of server-part model in SplitNN after training. The distilled model could be designed to fit the memory-limited Internet of Things (IoT) devices so that clients could get prediction results …
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Comparison of distributed training architecture for convolutional neural network in cloud
… of data and ever increasing model complexity of deep neural networks (DNNs) have enabled breakthroughs in various artificial intelligence fields such as computer vision, natural language processing and data mining. The training process of the DNN is a computationally intensive application that …
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Split learning on FPGAs
… At the same time, the field of machine learning is diversifying to include distributed deep learning methods like split learning, which can help preserve privacy by avoiding the sharing of raw data and model details. In order to continue to expand the capabilities of architectures like …
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Computational Privacy with Split Learning: Benchmarking of Algorithmic Defenses against Reconstruction Attacks
Distributed deep learning has potential for significant impact in preserving data privacy and improving model accuracy by leveraging massive sets of training data. However, passing intermediate weights, gradients, or activations is inherent in current distributed learning techniques, all of which …
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Efficient and Scalable Deep Learning
<p>Deep Neural Networks (DNNs) can achieve accuracy superior to traditional machine learning models, because of their large learning capacity and the availability of large amounts of labeled data. In general, larger DNNs can obtain higher accuracy. However, there are two obstacles which hinder us …
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Resource efficient distributed inference of deep neural networks for Edge AI
Deep Neural Networks (DNNs) have become the cornerstone of modern artificial intelligence, powering applications such as image recognition, language understanding, and multimodal reasoning. However, executing these models efficiently on edge devices remains a major challenge due to their high …
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Towards Efficient AI for Science in Scalable and High Performance Distributed System
… language processing. In recent years, machine learning methods have been increasingly applied to the scientific discovery process, accelerating advances in diverse fields. Notable examples include AlphaFold, which predicts protein structures, and ClimateX, which enhances weather prediction …