7 papers
Estimating Condition Number with Graph Neural Networks
Erin Carson, Xinye Chen
In this paper, we propose a fast method for estimating the condition number of sparse matrices using graph neural networks (GNNs). For efficient deployment of GNNs, we introduce a…
Computing k-means in mixed precision
Erin Carson, Xinye Chen, Xiaobo Liu
Motivated by the increasing availability of low- and mixed-precision arithmetic on modern hardware, we develop mixed-precision variants of Lloyd's algorithm for k-means clustering.…
Precision autotuning for linear solvers via contextual bandit-based RL
Erin Carson, Xinye Chen
We propose a reinforcement learning (RL) framework for adaptive precision tuning for linear solvers, which can be extended to general algorithms. The framework is formulated as a c…
pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
Erin Carson, Xinye Chen
Motivated by the growing demand for reduced-precision arithmetic in computational science, we exploit lower-precision emulation in Python---widely regarded as the dominant programm…
LLM-ABBA: Understanding time series via symbolic approximation
Xinye Chen, Erin Carson, Cheng Kang
The success of large language models (LLMs) for time series has been demonstrated in previous work. Utilizing a symbolic time series representation, one can efficiently bridge the…
Quantized symbolic time series approximation
Erin Carson, Xinye Chen, Cheng Kang
Time series are ubiquitous in numerous science and engineering domains, e.g., signal processing, bioinformatics, and astronomy. Previous work has verified the efficacy of symbolic…