Imagen de Portada

Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Obata, Kohei, Murayama, Taichi, Chen, Zheng, Matsubara, Yasuko, Sakurai, Yasushi
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies
por: Obata, Kohei, et al.
Publicado: (2025)

Dynamic Multi-Network Mining of Tensor Time Series
por: Obata, Kohei, et al.
Publicado: (2024)

Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series
por: Obata, Kohei, et al.
Publicado: (2024)

Selective Denoising Diffusion Model for Time Series Anomaly Detection
por: Obata, Kohei, et al.
Publicado: (2026)

Fredformer: Frequency Debiased Transformer for Time Series Forecasting
por: Piao, Xihao, et al.
Publicado: (2024)

D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams
por: Higashiguchi, Shingo, et al.
Publicado: (2025)

Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso
por: Higashiguchi, Shingo, et al.
Publicado: (2026)

Modeling Latent Non-Linear Dynamical System over Time Series
por: Fujiwara, Ren, et al.
Publicado: (2024)

Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series
por: Fujiwara, Ren, et al.
Publicado: (2026)

TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
por: Piao, Xihao, et al.
Publicado: (2026)

FredNormer: Frequency Domain Normalization for Non-stationary Time Series Forecasting
por: Piao, Xihao, et al.
Publicado: (2024)

Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streams
por: Kakio, Soshi, et al.
Publicado: (2026)

When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency
por: Fujiwara, Ren, et al.
Publicado: (2026)

Modeling Time-evolving Causality over Data Streams
por: Chihara, Naoki, et al.
Publicado: (2025)

Long-Term EEG Partitioning for Seizure Onset Detection
por: Chen, Zheng, et al.
Publicado: (2024)

Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams
por: Nakamura, Kota, et al.
Publicado: (2026)

Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages
por: Murayama, Taichi, et al.
Publicado: (2024)

CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems
por: Nakamura, Kota, et al.
Publicado: (2025)

SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning
por: Kotoge, Rikuto, et al.
Publicado: (2024)

ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
por: Kotoge, Rikuto, et al.
Publicado: (2025)

EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks
por: Kotoge, Rikuto, et al.
Publicado: (2025)

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
por: Chihara, Naoki, et al.
Publicado: (2026)

MLOmics: Cancer Multi-Omics Database for Machine Learning
por: Yang, Ziwei, et al.
Publicado: (2024)

Simple Contrastive Representation Learning for Time Series Forecasting
por: Zheng, Xiaochen, et al.
Publicado: (2023)

GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
por: Yang, Ziwei, et al.
Publicado: (2024)

TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
por: Chang, Ching, et al.
Publicado: (2023)

Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
por: Chang, Ching, et al.
Publicado: (2024)

From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals
por: Cai, Ruichu, et al.
Publicado: (2024)

Parametric Augmentation for Time Series Contrastive Learning
por: Zheng, Xu, et al.
Publicado: (2024)

Contrast All the Time: Learning Time Series Representation from Temporal Consistency
por: Shamba, Abdul-Kazeem, et al.
Publicado: (2024)

RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs
por: Jia, Haohui, et al.
Publicado: (2026)

Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
por: Li, Man, et al.
Publicado: (2024)

TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis
por: Liang, Zhiyu, et al.
Publicado: (2024)

Time Series Representation Learning with Supervised Contrastive Temporal Transformer
por: Liu, Yuansan, et al.
Publicado: (2024)

Fair Graph Representation Learning via Sensitive Attribute Disentanglement
por: Zhu, Yuchang, et al.
Publicado: (2024)

Data-driven Methods of Extracting Text Structure and Information Transfer
por: Honna, Shinichi, et al.
Publicado: (2025)

StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
por: Wu, Yu, et al.
Publicado: (2024)

AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification
por: Chen, Yuxuan, et al.
Publicado: (2025)

PINNs Failure Modes are Overfitting
por: Andersen, Nigel T., et al.
Publicado: (2026)

Disentangled Representation Learning via Flow Matching
por: Chi, Jinjin, et al.
Publicado: (2026)