5 papers
HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression
Yuxuan Yang, Feiyang Ren, Bowen Zeng +4
Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top- nucleus sampling) offer superior accuracy by dynamically fluctuating me…
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
Yuxuan Yang, Dalin Zhang, Yuxuan Liang +3
Time Series Forecasting (TSF) is a crucial task in various domains, yet existing TSF models rely heavily on high-quality data and insufficiently exploit all available data. This pa…
Diagnosis of Fuel Cell Health Status with Deep Sparse Auto-Encoder Neural Network
Chenyan Fei, Dalin Zhang, Chen Melinda Dang
Effective and accurate diagnosis of fuel cell health status is crucial for ensuring the stable operation of fuel cell stacks. Among various parameters, high-frequency impedance ser…
Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning
Minbo Ma, Kai Tang, Huan Li +3
Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number of variables, but in real-world applications, Cyb…
Conditional Lagrangian Wasserstein Flow for Time Series Imputation
Weizhu Qian, Dalin Zhang, Yan Zhao +1
Time series imputation is important for numerous real-world applications. To overcome the limitations of diffusion model-based imputation methods, e.g., slow convergence in inferen…