To speed up computation, deep neural networks (DNNs) usually rely on highly optimized tensor operators. Despite the effectiveness, tensor operators are often defined empirically with ad hoc semantics.
Abstract: This work considers three main problems related to fast finite-iteration convergence (FIC), nonrepetitive uncertainty, and data-driven design. A data-driven robust finite-iteration learning ...
Abstract: High-dimensional multi-omics data present critical challenges, including overfitting, class imbalance, and information leakage, limiting reproducible biomarker discovery in precision ...
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