5 citations · 5 across the 6 of their papers we have counts for
7 papers
CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
Yaohua Zha, Chunlin Fan, Peiyuan Liu +4
Multi-channel time-series data, prevalent across diverse applications, is characterized by significant heterogeneity in its different channels. However, existing forecasting models…
Generalization Bounds for Transformer Channel Decoders
Qinshan Zhang, Bin Chen, Yong Jiang +1
Transformer channel decoders, such as the Error Correction Code Transformer (ECCT), have shown strong empirical performance in channel decoding, yet their generalization behavior r…
Logic-of-Thought: Empowering Large Language Models with Logic Programs for Solving Puzzles in Natural Language
Naiqi Li, Peiyuan Liu, Zheng Liu +3
Solving puzzles in natural language poses a long-standing challenge in AI. While large language models (LLMs) have recently shown impressive capabilities in a variety of tasks, the…
Efficient Differentiable Approximation of Generalized Low-rank Regularization
Naiqi Li, Yuqiu Xie, Peiyuan Liu +3
Low-rank regularization (LRR) has been widely applied in various machine learning tasks, but the associated optimization is challenging. Directly optimizing the rank function under…
Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing
Bin Chen, Wenbo Yu, Qinshan Zhang +4
Interactive computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-ori…
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Peiyuan Liu, Hang Guo, Tao Dai +5
Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a…
WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
Peiyuan Liu, Beiliang Wu, Naiqi Li +5
Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourie…