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  • MICN: Multi-scale Local and Global Context Modeling for Long-term . . .
    MICN yields impressive 18 2% and 24 5% relative improvements for multivariate and univariate time series, respectively This paper uses a lot of ablation experiments to verify that the proposed Multi-scale Isometric Convolution outperforms the self-attention family and Auto-correlation mechanism
  • MICN: M LOCAL AND GLOBAL CONTEXT MODELING FOR L TERM . . . - OpenReview
    ABSTRACT Recently, Transformer-based methods have achieved surprising performance in the field of long-term series forecasting, but the attention mechanism for computing global correlations entails high complexity And they do not allow for targeted modeling of local features as CNN structures do To solve the above problems, we propose to combine local features and global correlations to
  • Forum - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • Towards Multi-dimensional Explanation Alignment for Medical. . .
    To address these limitations, we propose a novel framework called Med-MICN (Medical Multi-dimensional Interpretable Concept Network) Med-MICN provides interpretability alignment for various angles, including neural symbolic reasoning, concept semantics, and saliency maps, which are superior to current interpretable methods
  • Feihu Huang - OpenReview
    Publications MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao Published: 01 Feb 2023, Last Modified: 15 Feb 2023 ICLR 2023 notable top 5% Dateformer: Transformer Extends Look-back Horizon to Predict Longer-term Time Series
  • Huiqiang Wang - OpenReview
    Huiqiang Wang, Jieming Shi, Li Qing 17 Sept 2025 (modified: 11 Feb 2026) Submitted to ICLR 2026 MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao Published: 01 Feb 2023, Last Modified: 15 Feb 2023 ICLR 2023 notable top 5%
  • LONG TERM TIME SERIES FORECASTING WITH VI SION TRANSFORMER
    1 INTRODUCTION Long-term Time Series Forecasting (LTSF) provides crucial support and guidance for various do-mains such as intelligent transportation (Li Zhu, 2021; Rao et al , 2022), smart manufacturing (Zi et al , 2021; Wang et al , 2022b), and healthcare (Wang et al , 2022a; Shokouhifar Ranjbarimesan, 2022), and it poses significant challenges due to complex long-term dependencies
  • Periodicity Decoupling Framework for Long-term Series Forecasting
    This paper decouples variations of different scales in multi-variate long-term time series based on their periodicity It then leverages the respective modeling strengths of CNNs and Transformer models to represent these scale-distinct variations Extensive experiments on multiple long-term time series forecasting datasets demonstrate that the proposed Periodic Decoupling Framework (PDF
  • Interest-based Item Representation Framework for Recommendation. . .
    We propose a framework to learn interest-based item representations directly by introducing user Multi Interests Capsule Network (MICN) To make the framework model-agnostic, user Multi Interests Capsule Network is designed as an auxiliary task to jointly learn item-based item representations and interest-based item representations
  • TIMEM : DECOMPOSABLE MULTISCALE MIXING FOR T S FORECASTING - OpenReview
    omposition as the pre-processing before linear regression MICN (Wang et al , 2023) also decomposes input series into seasonal and trend terms, and th n integrates the global and local context for forecasting As for the multi-periodicity analysis, N-BEATS (Oreshkin et al , 2019) fits t





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