1 citations · 1 across the 8 of their papers we have counts for
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Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification
Jiahui Li, Jianfeng Shan, Wenpei Chen +5
Test-time reinforcement learning has emerged as a promising paradigm for enhancing the complex reasoning abilities of large language models in a completely label-free manner. Despi…
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
Shunyu Wu, Jiawei Huang, Weibin Feng +6
Time series foundation models (TSFMs) have recently achieved remarkable success in universal forecasting by leveraging large-scale pretraining on diverse time series data. Compleme…
Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
Shunyu Wu, Tianyue Li, Yixuan Leng +4
Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the q…
Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment
Shunyu Wu, Dan Li, Wenjie Feng +3
High-quality time series (TS) data are essential for ensuring TS model performance, rendering research on rating TS data quality indispensable. Existing methods have shown promisin…
BACE-RUL: A Bi-directional Adversarial Network with Covariate Encoding for Machine Remaining Useful Life Prediction
Zekai Zhang, Dan Li, Shunyu Wu +4
Prognostic and Health Management (PHM) are crucial ways to avoid unnecessary maintenance for Cyber-Physical Systems (CPS) and improve system reliability. Predicting the Remaining U…