6 accepted papers
Neural Forecasters (NFs) have become a cornerstone of Long-term Time Series Forecasting (LTSF). However, recent progress has been hampered by an overemphasis on architectural complexity at the expense of fundamental forecasting principles. In this work, we revisit the principles of LTSF. We begin by…
Understanding the behaviors of robotic arms is essential for various robotic applications such as logistics management, precision agriculture, and automated manufacturing. However, the lack of large-scale and diverse datasets significantly hinders progress in video-based robotic arm action understan…
Retrieval-Augmented Generation (RAG), by integrating non-parametric knowledge from external knowledge bases into models, has emerged as a promising approach to enhancing response accuracy while mitigating factual errors and hallucinations. This method has been widely applied in tasks such as Questio…
Multiple Kernel k-means and its variants integrate a group of kernels to improve clustering performance, but it still has some drawbacks: 1) linearly combining base kernels to get the optimal one limits the kernel representability and cuts off the negotiation of kernel learning and clustering; 2) ig…
Multiple kernel k-means clustering (MKKM) and its variants have been thoroughly studied over the past decades. However, most existing models utilize a spectrum-based two-step approach to solve the clustering objective, which may deviate from the final cluster labels and lead to suboptimal performanc…
The multiple kernel k-means (MKKM) and its variants utilize complementary information from different kernels, achieving better performance than kernel k-means (KKM). However, the optimization procedures of previous works all comprise two stages, learning the continuous relaxed label matrix and obtai…