In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilistic modeling, while ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization capabilities. By ada...
A novel pattern-guided mixture-of-experts that expands model capacity through sparse activation and uses shallow patch similarities to constrain deep-layer routing, guiding expert specialization across heterogeneous time series and an implicit quantile network head that predicts arbitrary quantiles to characterize pred...
Xingjian Wu, Chen-Juan Guo, Xiangfei Qiu et al.· 0 citations
This work proposes PolarFormer, a novel framework for time series generation that addresses limitations through a polar decomposition-based discrete representation, and proposes a structurally decoupled generation strategy that models radial and angular token sequences jointly while leveraging their orthogonality to su...
Jiahong Lyu, Hongfan Gao, Wang-Meng Shen et al.· Proceedings of the 32nd ACM...· 1 citation
Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including mobility analysis, public safety forecasting, and service demand estimation. Existing multi-view methods typically improve region embeddings...
Sean Bin Yang, Ying Sun, Zong-Yi Xu et al.· 0 citations
Computational prediction of crystal properties plays a pivotal role in materials science. With the accelerated progress in machine learning, crystal property prediction has seen remarkable advancements. Nevertheless, the utilization of machine learning in this context faces several challenges. First, existing methods...
Haomin Yu, Jilin Hu, K. Tolborg et al.· ACM Transactions on AI for S...· 0 citations
Time series generation is essential for data augmentation and privacy-preserving analysis across many real-world domains. Recent progress in discrete token modeling~(DTM) has demonstrated strong potential by transforming continuous sequences into discrete representations and performing generation in the latent space. H...
Jiahong Lyu, Hongfan Gao, Wangmeng Shen et al.· Proceedings of the 32nd ACM...· 0 citations
ST-EVO is proposed, which supports dialogue-wise communication scheduling with a compact yet powerful flow-matching based Scheduler to make precise Spatio-Temporal scheduling, and can also perceive the uncertainty of MAS, and possesses self-feedback ability to learn from accumulated experience.
Xingjian Wu, Xvyuan Liu, Junkai Lu et al.· arXiv.org· 2 citations
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