Contrastive learning has become a dominant paradigm for learning time series representations from large-scale unlabeled data. However, current methods are often adapted from computer vision and rely on random time-domain augmentations (e.g., jittering and cropping). Such augmentations can unpredictably disrupt the natu...
Yu-Sen Liu, Zhichen Lai, Hua Lu et al.· Proceedings of the Thirty-Fi...· 0 citations
Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among variables. Existing methods often struggle to capture these complexities, resulting in inaccurate causal structures. To address this issue, we propose a novel framework tha...
Yusen Liu, Yong Wang, Yifan Yin et al.· Pacific-Asia Conference on K...· 5 citations
Crystal Structure Prediction (CSP), the task of determining stable atomic arrangements from chemical composition alone, remains a central challenge in computational materials science with direct implications for accelerating materials discovery. While recent diffusion-based generative models achieve impressive results...
Lu Yang, Tiantian Xu, Xiufeng Liu et al.· Proceedings of the 32nd ACM...· 0 citations
Crystal Structure Prediction (CSP), the task of determining stable atomic arrangements from chemical composition alone, remains a central challenge in computational materials science with direct implications for accelerating materials discovery. While recent diffusion-based generative models achieve impressive results...
Lu Yang, Tiantian Xu, X. Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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