Sep 28, 2024 · To tackle these issues, we propose the Non-sparse Classifier Evolution framework (NsCE) to facilitate effective global discriminative feature learning with ...
Sep 21, 2024 · In this article, we'll delve into three popular regularization methods: Dropout, L-Norm Regularization, and Batch Normalization. We'll explore each technique's ...
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Sep 10, 2024 · In this paper, we prove two alternative views of discount regularization that expose unintended consequences and motivate novel regularization methods. In model ...
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Sep 15, 2024 · Lecture #11 discusses GPU sparsity, specifically semi-structured and block sparsity techniques, for accelerating neural network inference and training by ...
Sep 13, 2024 · This paper's extensive investigation across scenarios reveals that most SNNs trained on challenging samples can often match or surpass dense models in accuracy ...
Sep 14, 2024 · In this paper, we propose a novel method called joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering (JKHR). Our ...
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Oct 1, 2024 · Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They ...
Sep 23, 2024 · Sparse regularization, such as the problems ( P j ) , forms the core of many start-of-the-art reconstruction algorithms for inverse problems. We now ...
Oct 1, 2024 · Kernel PLS is efficient in modelling non-linear predictor-response dependencies. •. Parameter optimization of the Kernel function is essential in Kernel PLS.
Sep 14, 2024 · We consider a high-dimensional linear regression problem. Unlike many papers on the topic, we do not require sparsity of the regression coefficients; ...
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