Title: NeuMatC: A General Neural Framework For Fast 	Parametric Matrix Operation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Low-rank representation;continuous representation;parameterized matrix computation;tensor factorization;machine learning

Abstract: Matrix operations (e.g., inversion and singular value decomposition (SVD)) are fundamental in science and engineering. In many emerging real-world applications (such as wireless communication and signal processing), these operations must be performed repeatedly over matrices with parameters varying continuously.  However, conventional methods tackle each matrix operation independently, underexploring the inherent low-rankness  and continuity along the parameter dimension, resulting in significantly redundant computation.  To address this challenge, we propose \textbf{\textit{Neural Matrix Computation Framework} (NeuMatC)}, which elegantly tackles general parametric matrix operation tasks by leveraging  the underlying low-rankness and continuity along the parameter dimension. Specifically, NeuMatC unsupervisedly learns a low-rank and continuous mapping from parameters to their corresponding matrix operation results. Once trained, NeuMatC enables efficient computations at arbitrary parameters using only a few basic operations (e.g., matrix multiplications and nonlinear activations), significantly reducing redundant computations. Experimental results on both synthetic and real-world datasets demonstrate the promising performance of NeuMatC, exemplified by over $3\times$ speedup in parametric inversion and $10\times$ speedup in parametric SVD compared to the widely used NumPy baseline in wireless communication, while maintaining acceptable accuracy.