3 accepted papers
Weight Quantization (WQ) is a key technique for lightweight Deep Neural Network (DNN) computations. While existing algorithms often pursue memory compression and inference acceleration with accuracy comparable to full-precision models, the effect of WQ on DNN uncertainty remains largely unexplored.
Time series forecasting (TSF) is crucial in various fields and has gained extensive research. However, most studies are conducted based on TS data with scale homogeneity. This paper proposes a self-Adaptive Scale-handling (AS) module to improve the performance of forecasting TS with scale heterogene…
Positive and Unlabeled learning (PU learning) aims to build a binary classifier where only positive and unlabeled data are available for classifier training. However, existing PU learning methods all work on a batch learning mode, which cannot deal with the online learning scenarios with sequential…