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Showing 1 to 6 of 6 for “"Efficient Neural Networks"”.

  1. Robust and efficient neural networks: Algorithms, architectures, and circuits

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms

    uiuc Repository record for Robust and efficient neural networks: Algorithms, architectures, and circuits (opens in a new tab)

  2. Software and Hardware Co-design for Efficient Neural Networks

    Deep Neural Networks (DNNs) offer state-of-the-art performance in many domains but this success comes at the cost of high computational and memory resources. Since DNN inference is now a popular workload on both edge and cloud systems, there is an urgent need to improve its energy efficiency. The …

    cambridge Repository record for Software and Hardware Co-design for Efficient Neural Networks (opens in a new tab)

  3. Beyond Memorization: Exploring the Dynamics of Grokking in Sparse Neural Networks

    … learning, "grokking" is a phenomenon where neural network models demonstrate a sudden improvement in generalization, distinct from traditional learning phases, long after the initial training appears complete. This behavior was first identified by Power et al. (2022) [5]. This thesis …

    mit Repository record for Beyond Memorization: Exploring the Dynamics of Grokking in Sparse Neural Networks (opens in a new tab)

  4. Efficient Deep Learning: From Theory to Practice

    Modern machine learning often relies on deep neural networks that are prohibitively expensive in terms of the memory and computational footprint. This in turn significantly inhibits the potential range of applications where we are faced with non-negligible resource constraints, e.g., real-time data …

    mit Repository record for Efficient Deep Learning: From Theory to Practice (opens in a new tab)

  5. Online Machine Learning for Wireless Communications: Channel Estimation, Receive Processing, and Resource Allocation

    … applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training data in communication networks, especially in physical and MAC layer, are extremely limited; 2). The high-dynamic wireless environment and fast changing …

    vt Repository record for Online Machine Learning for Wireless Communications: Channel Estimation, Receive Processing, and Resource Allocation (opens in a new tab)

  6. Unsupervised sound separation

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms

    uiuc Repository record for Unsupervised sound separation (opens in a new tab)