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Harvard University(哈佛大学)

2026-06-26 至 2026-06-26 共收录 2
2602.16220 2026-06-26 cs.LG 版本更新

SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting

SEMixer: 语义增强的MLP-Mixer用于多尺度混合和长期时间序列预测

Xu Zhang, Qitong Wang, Peng Wang, Wei Wang

机构 * Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence Fudan University(上海数据科学 key 实验室,复旦大学计算机科学与人工智能学院) Harvard University(哈佛大学)

AI总结 提出SEMixer模型,通过随机注意力机制和多尺度渐进混合链,有效建模多尺度时间依赖并解决语义鸿沟问题,在10个公开数据集和真实无线网络数据上取得优异性能。

Comments This work is accepted by the proceedings of the ACM Web Conference 2026 (WWW 2026). The code is available at the link https://github.com/Meteor-Stars/SEMixer

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2502.18966 2026-06-26 cs.LG 版本更新

Bayesian Optimization for General Reaction Conditions

通用反应条件的贝叶斯优化

Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alán Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff

机构 * Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich(苏黎世联邦理工学院化学与生物工程学院,化学与应用生物科学系) NCCR Catalysis, Switzerland(瑞士催化联合体) Department of Chemistry, University of Toronto(多伦多大学化学系) Vector Institute, Toronto, Canada(多伦多向量研究所) Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School(哈佛医学院生物化学与分子药理学系) Dana-Farber Cancer Institute, Boston, MA, USA(波士顿马萨诸塞州 Dana-Farber 癌症研究所) School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University(南洋理工大学化学系、化工与生物技术学院) Department of Computer Science, University of Toronto(多伦多大学计算机科学系) Department of Chemical Engineering and Applied Chemistry, University of Toronto(多伦多大学化学工程与应用化学系) Department of Materials Science and Engineering, University of Toronto(多伦多大学材料科学与工程系) Acceleration Consortium, University of Toronto(多伦多大学加速联盟) Canadian Institute for Advanced Research (CIFAR)(加拿大高级研究研究院) Institute of Medical Science, Medical Sciences Building, Toronto, Canada(多伦多大学医学科学研究院,医学科学大楼) NVIDIA, Toronto, Canada(多伦多NVIDIA) Department of Computer Science, Western University(温哥华大学计算机科学系)

AI总结 提出CurryBO框架,通过curried函数的贝叶斯优化实现通用反应条件的高效搜索,在多个基准上显著提升样本效率。

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