Blind Source Separation Techniques

Derniers papiers

• Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models

  Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin · 14 septembre 2026

  Diffusion probabilistic models generate samples by learning to reverse a noise-injection process that transforms data into noise. A key development is the reformulation of the reverse sampling process as a deterministic probability flow ordinary differential equation (ODE), which allows for efficien…

• Foundations of Independent Component Analysis

  Patrick Forr\'e · 14 août 2026

  We present the mathematical foundations of linear independent component analysis (ICA) models based on standard literature in a self-contained note. It is aimed at readers with a background in measure-theoretic probability theory. We first develop the theory of the characteristic functions of probab…

• Linear Independent Component Analysis via Optimal Transport

  Ashutosh Jha, Michel Besserve, Simon Buchholz · 16 juillet 2026

  Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures. To achieve this, classical ICA algorithms attempt to maximize non-Gaussianity, measured by negentropy, which is linked to independence by information theory. Because exact negentropy o…

• Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution

  Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi · 2 juillet 2026

  Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysomnography are costly and impractical for long-term, home-based monitoring. This study presents an energy-efficient approach for detecting four sleep st…

• Nonlinear mixture model motivated subspace clustering

  Ivica Kopriva · 30 juin 2026

  We derive the linear union-of-subspaces (UoS) model for subspace clustering (SC) from the nonlinear mixture model (NMM) used in blind source separation (BSS) to represent a D-dimensional observation vector as an unknown multivariate nonlinear mapping of C latent variables. Assuming the mapping is di…

• Region-Adaptive Sampling for Diffusion Transformers

  Ziming Liu, Yifan Yang, Chengruidong Zhang, Yiqi Zhang, Lili Qiu, Yang You, Yuqing Yang · 16 juin 2026

  Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains. However, their reliance on multiple sequential forward passes significantly limits real-time performance. Previous acceleration methods have primarily focused on reducing the number of sampling steps o…

• Normative Networks for Source Separation via Local Plasticity and Dendritic Computation

  Bariscan Bozkurt, Efe Ali Gorguner, Francesco Innocenti, Rafal Bogacz · 20 mai 2026

  Blind source separation (BSS) is a natural framework for studying how latent causes may be recovered from sensory mixtures, but deriving online and biologically plausible algorithms for structured (i.e., constrained to known domains) and potentially correlated sources remains challenging. Recent wor…

• bispectrum: Selective $G$-Bispectra Made Practical

  Johan Mathe, Adele Myers, Simon Mataigne, Nina Miolane · 11 mai 2026

  Many machine learning tasks are invariant under the action of a group $G$ of transformations: signal classification can be invariant under translations, image classification under 2D rotations, and spherical-image classification under 3D rotations. The $G$-bispectrum is a principled complete invaria…

• StrEBM: A Structured Latent Energy-Based Model for Blind Source Separation

  Yuan-Hao Wei · 21 avril 2026

  This paper proposes StrEBM, a structured latent energy-based model for source-wise structured representation learning. The framework is motivated by a broader goal of promoting identifiable and decoupled latent organization by assigning different latent dimensions their own learnable structural bias…

• HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention

  Miguel Camelo Botero, Esra Aycan Beyazit, Nina Slamnik-Krije\v{s}torac, Johann M. Marquez-Barja · 17 avril 2026

  Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight…

• Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds

  Yuwen Jiang · 13 avril 2026

  Independent Component Analysis (ICA) is a fundamental unsupervised learning technique foruncovering latent structure in data by separating mixed signals into their independent sources. While substantial progress has been made in establishing asymptotic identifiability guarantees for nonlinear ICA, t…

• StrADiff: A Structured Source-Wise Adaptive Diffusion Framework for Linear and Nonlinear Blind Source Separation

  Yuan-Hao Wei · 8 avril 2026

  This paper presents a Structured Source-Wise Adaptive Diffusion Framework for linear and nonlinear blind source separation. The framework interprets each latent dimension as a source component and assigns to it an individual adaptive diffusion mechanism, thereby establishing source-wise latent model…

• Time-Correlated Video Bridge Matching

  Viacheslav Vasilev, Arseny Ivanov, Nikita Gushchin, Maria Kovaleva, Alexander Korotin · 27 mars 2026

  Diffusion models excel in noise-to-data generation tasks, providing a mapping from a Gaussian distribution to a more complex data distribution. However they struggle to model translations between complex distributions, limiting their effectiveness in data-to-data tasks. While Bridge Matching models …

• PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA

  Yuan-Hao Wei, Yan-Jie Sun · 26 mars 2026

  Independent component analysis is a core framework within blind source separation for recovering latent source signals from observed mixtures under statistical independence assumptions. In this work, we propose PDGMM-VAE, a source-oriented variational autoencoder in which each latent dimension, inte…

• Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts

  Lars van der Laan, Marco Carone, Alex Luedtke · 20 mars 2026

  We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle efficiency as Neyman-orthogonal learning strategies, …

• AR-Flow VAE: A Structured Autoregressive Flow Prior Variational Autoencoder for Unsupervised Blind Source Separation

  Yuan-Hao Wei, Fu-Hao Deng, Lin-Yong Cui, Yan-Jie Sun · 17 mars 2026

  Blind source separation (BSS) seeks to recover latent source signals from observed mixtures. Variational autoencoders (VAEs) offer a natural perspective for this problem: the latent variables can be interpreted as source components, the encoder can be viewed as a demixing mapping from observations t…

• Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Code

  Yicheng Wu, Tao Song, Zhonghua Wu, Jin Ye, Zongyuan Ge, Wenjia Bai, Zhaolin Chen, Jianfei Cai · 17 mars 2026

  Magnetic resonance imaging (MRI) is a powerful and versatile imaging technique, offering a wide spectrum of information about the anatomy by employing different acquisition modalities. However, in the clinical workflow, it is impractical to collect all relevant modalities due to the scan time and co…

• EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation

  Shih-Yang Liu, Maksim Khadkevich, Nai Chit Fung, Charbel Sakr, Chao-Han Huck Yang, Chien-Yi Wang, Saurav Muralidharan, Hongxu Yin, Kwang-Ting Cheng, Jan Kautz, Yu-Chiang Frank Wang, Pavlo Molchanov, Min-Hung Chen · 12 mars 2026

  While post-training compression techniques effectively reduce the memory footprint, latency, and power consumption of Large Language Models (LLMs), they often result in noticeable accuracy degradation and remain limited by hardware and kernel constraints that restrict supported compression formats u…

• Self-Aug: Query and Entropy Adaptive Decoding for Large Vision-Language Models

  Eun Woo Im, Muhammad Kashif Ali, Vivek Gupta · 4 mars 2026

  Large Vision-Language Models (LVLMs) have demonstrated remarkable multimodal capabilities, but they inherit the tendency to hallucinate from their underlying language models. While visual contrastive decoding has been proposed to mitigate this issue, existing methods often apply generic visual augme…

• Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability

  Taylor Sorensen, Benjamin Newman, Jared Moore, Chan Park, Jillian Fisher, Niloofar Mireshghallah, Liwei Jiang, Yejin Choi · 4 mars 2026

  Language model post-training has enhanced instruction-following and performance on many downstream tasks, but also comes with an often-overlooked cost on tasks with many possible valid answers. On many tasks such as creative writing, synthetic data generation, or steering to diverse preferences, mod…

• Reservoir Subspace Injection for Online ICA under Top-n Whitening

  Wenjun Xiao, Yuda Bi, Vince D Calhoun · 3 mars 2026

  Reservoir expansion can improve online independent component analysis (ICA) under nonlinear mixing, yet top-$n$ whitening may discard injected features. We formalize this bottleneck as \emph{reservoir subspace injection} (RSI): injected features help only if they enter the retained eigenspace withou…

• Provable Subspace Identification of Nonlinear Multi-view CCA

  Zhiwei Han, Stefan Matthes, Hao Shen · 2 mars 2026

  We investigate the identifiability of nonlinear Canonical Correlation Analysis (CCA) in a multi-view setup, where each view is generated by an unknown nonlinear map applied to a linear mixture of shared latents and view-private noise. Rather than attempting exact unmixing, a problem proven to be ill…

• A 1/R Law for Kurtosis Contrast in Balanced Mixtures

  Yuda Bi, Wenjun Xiao, Linhao Bai, Vince D Calhoun · 27 février 2026

  Kurtosis-based Independent Component Analysis (ICA) weakens in wide, balanced mixtures. We prove a sharp redundancy law: for a standardized projection with effective width $R_{\mathrm{eff}}$ (participation ratio), the population excess kurtosis obeys $|\kappa(y)|=O(\kappa_{\max}/R_{\mathrm{eff}})$, …

• Nonparametric Evaluation of Noisy ICA Solutions

  Syamantak Kumar, Purnamrita Sarkar, Peter Bickel, Derek Bean · 9 février 2026

  Independent Component Analysis (ICA) was introduced in the 1980's as a model for Blind Source Separation (BSS), which refers to the process of recovering the sources underlying a mixture of signals, with little knowledge about the source signals or the mixing process. While there are many sophistica…

• Bayesian Matrix Decomposition and Applications

  Jun Lu · 9 février 2026

  The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover al…