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Found 4 papers, 1 papers with code
Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories
In this paper, we propose an effective federated learning method named ScalableFL, where the depths and widths of the local models for each client are adjusted according to the clients' input image size and the numbers of output categories.
Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
Compressing DNNs is important for the real-world applications operating on resource-constrained devices.
Scalable Deep Neural Networks via Low-Rank Matrix Factorization
Compressing deep neural networks (DNNs) is important for real-world applications operating on resource-constrained devices.
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks
In recent years, deep neural networks (DNNs) have been applied to various machine leaning tasks, including image recognition, speech recognition, and machine translation.
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