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Found 3 papers, 1 papers with code
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression
This representer theorem establishes that shallow vector-valued neural networks are the solutions to data-fitting problems over these infinite-dimensional spaces, where the network widths are bounded by the square of the number of training data.
PathProx: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks
Weight decay is one of the most widely used forms of regularization in deep learning, and has been shown to improve generalization and robustness.
A Guide to Computational Reproducibility in Signal Processing and Machine Learning
Computational reproducibility is a growing problem that has been extensively studied among computational researchers and within the signal processing and machine learning research community.
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