DGCPath is presented, an innovative Distribution-aware Generative Contrastive learning framework for Path representation that establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings.
Sean Bin Yang, Hao Miao, Zong-Yi Xu et al.· Proceedings of the Thirty-Fi...· 1 citation
The notion of a crystal supercell is introduced, which enables more comprehensive explorations of crystal structure and offers evidence that SILVA is capable of outperforming state-of-the-art methods.
Haomin Yu, Ji-Lin 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
