6 papers · 1 filter
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…
QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregation
Erin Carson, Xinye Chen, Fei He +1
The expansion of time-series data from sensors and monitoring systems has made compact representations increasingly important. Such representations should retain signal structure w…
Mixed precision HODLR matrices
Erin Carson, Xinye Chen, Xiaobo Liu
Hierarchical matrix computations have attracted significant attention in the science and engineering community as exploiting data-sparse structures can significantly reduce the com…
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.…
Quantized Embedding Vectors for Controllable Diffusion Language Models
Cheng Kang, Xinye Chen, Yong Hu +1
Improving the controllability, portability, and inference speed of diffusion language models (DLMs) is a key challenge in natural language generation. While recent research has sho…
Parallel Two-Stage Approach for Joint Symbolic Approximation of Time Series
Xinye Chen
As time-series applications grow larger, there is increasing demand for symbolic representations that are compact, accurate, and scalable across many signals and computing resource…