arXiv Machine Learning

Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models

arXiv:2607. 07008v1 Announce Type: cross Abstract: A common method for the representation and analysis of time-series data is the hidden Markov model (HMM), where each observation is associated with a hidden state that evolves over time.

arXiv Machine Learning

Sep 16

High-Performance Tensor Formulation of the Viterbi Algorithm for Hidden Semi-Markov Models

The paper introduces a tensor-based formulation of the Viterbi algorithm for Hidden Semi-Markov Models (HSMMs), converting inner loops into tensor operations that align with SIMD and massively parallel architectures. It presents optimized implementations for single- and multi-core CPUs and, for the first time, GPUs. Experiments show speedups of up to 14× on a single core, over 200× with multi-core, and more than 570× on GPU compared to the sequential baseline, setting a new performance benchmark for large-scale HSMM decoding.

By Lorenzo Piarulli, Elia Belli, Daniele De Sensi

benchmarks

arXiv AI

Aug 17

Your Probabilistic JEPA Is Secretly a Hidden Markov Model: A State-Space Interpretation of Joint-Embedding Predictive Learning

arXiv:2608. 13621v1 Announce Type: new Abstract: A hidden Markov model (HMM) combines three roles: inference of a hidden-state belief from observations, propagation through a Markov transition, and emission back to observation space.

By Yongchao Huang

rag

arXiv Statistics ML

Sep 17

CP-factorization for high dimensional tensor time series and double projection iterations

arXiv:2606.08560v2 Announce Type: replace-cross Abstract: We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate...

By Jinyuan Chang, Guanglin Huang, Qiwei Yao, Long Yu

efficiency

arXiv Machine Learning

Aug 4

PyDPF: A Python Package for Differentiable Particle Filtering

arXiv:2510. 25693v3 Announce Type: replace-cross Abstract: State-space models (SSMs) are a widely used tool in time series analysis.

By John-Joseph Brady, Benjamin Cox, Yunpeng Li, V\'ictor Elvira

arXiv Machine Learning

Jun 10

Structured Adaptive Tensor Prediction for Streaming Data

arXiv:2606. 10085v1 Announce Type: new Abstract: Matrix-valued time series arise in a wide range of applications, such as spatio-temporal data from medical imaging and geophysics.

By Zhen Qin, Yang Chen

efficiency

arXiv Machine Learning

5d ago

Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection

The paper introduces a Physics-informed Predictive Prior Tensor Autoencoder (PPPTAE) for anomaly detection in multi-dimensional time series. It combines reconstruction-based and prediction-based autoencoders by embedding a Bayesian fusion approach and a predictive prior that respects tensor correlations. The method incorporates physical laws via tensor low‑rank decomposition to prevent over‑generalization and is validated on real‑world datasets.

By Jianan Liu, Chunguang Li

arXiv Machine Learning

Aug 6

MINT: Tensor Decomposition on Stacked Recurrence Matrices for Time Series Data Mining

arXiv:2608. 04157v1 Announce Type: new Abstract: Recurrence plots are a time series data mining primitive applied to a variety of domains (e.

By Kaamil Kaka, Audrey Der, Evangelos E. Papalexakis, Zachary Zimmerman, Vikram Jayaram

arXiv Machine Learning

Jun 30

AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models

arXiv:2606. 28708v1 Announce Type: cross Abstract: Accurately explaining hidden patterns in multi-aspect data has typically been done by leveraging labels and/or accompanying auxiliary metadata.

By Dawon Ahn, Auder Der, Evangelos E. Papalexakis

llms

arXiv Computer Vision

Sep 15

Tensor Decomposition Structure Search Framework from an Interaction Perspective

arXiv:2603.02720v2 Announce Type: replace Abstract: Recently, tensor decompositions have attracted increasing attention. Fundamentally, different interactions among factors induce distinct tensor dec...

By Ting-Wei Zhou, Xi-Le Zhao, Sheng Liu, Wei-Hao Wu, Yu-Bang Zheng, Deyu Meng

benchmarks

arXiv Machine Learning

Jul 1

Online TT-ALS for Streaming Tensor Decomposition with Incremental Orthogonalization

arXiv:2606. 31061v1 Announce Type: cross Abstract: Tensor Train (TT) decomposition is a powerful technique for analyzing high-dimensional data.

By Hiroki Takeda, Yuto Miyatake, Daisuke Furihata

arXiv Machine Learning

Jul 27

Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis

arXiv:2607. 22262v1 Announce Type: cross Abstract: Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spatial and temporal patterns exhibit rich variability across subjects.

By Laura M. Montaldo, Ricardo A. Borsoi, Sebastian Miron, Tulay Adali

benchmarks

arXiv Machine Learning

Jun 10

Time series forecasting from partial observations via Non-negative Matrix Factorization

arXiv:2102. 05314v2 Announce Type: replace Abstract: In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values.

By Yohann de Castro (ICJ, PSPM, CERMICS UMR 9032, ECL, IUF), Luca Mencarelli (CERMICS UMR 9032)

llms benchmarks

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