Image de couverture de livre

Adaptive Block Sparse Regularization under Arbitrary Linear Transform

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Furuhashi, Takanobu, Hontani, Hidekata, Yokota, Tatsuya
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Signal Processing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

WEEP: A Differentiable Nonconvex Sparse Regularizer via Weakly-Convex Envelope
par: Furuhashi, Takanobu, et autres
Publié: (2025)

Nonconvex Latent Optimally Partitioned Block-Sparse Recovery via Log-Sum and Minimax Concave Penalties
par: Furuhashi, Takanobu, et autres
Publié: (2026)

Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
par: Yokota, Haruki, et autres
Publié: (2025)

Very Basics of Tensors with Graphical Notations: Unfolding, Calculations, and Decompositions
par: Yokota, Tatsuya
Publié: (2024)

Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming
par: Yokota, Haruki, et autres
Publié: (2024)

Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator
par: Mahadi, Maaz, et autres
Publié: (2024)

Time-Varying Graph Learning with Constraints on Graph Temporal Variation
par: Yokota, Haruki, et autres
Publié: (2020)

Advancing Single-Snapshot DOA Estimation with Siamese Neural Networks for Sparse Linear Arrays
par: Zheng, Ruxin, et autres
Publié: (2025)

Automatic Regularization for Linear MMSE Filters
par: Zanco, Daniel Gomes de Pinho, et autres
Publié: (2023)

Piecewise Linearity of Min-Norm Solution Map of a Nonconvexly Regularized Convex Sparse Model
par: Zhang, Yi, et autres
Publié: (2023)

L1 Regularization Paths in Linear Models by Parametric Gaussian Message Passing
par: Li, Yun-Peng, et autres
Publié: (2026)

Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology
par: Chen, Kuan-Lin, et autres
Publié: (2024)

Adaptive function approximation based on the Discrete Cosine Transform (DCT)
par: Pérez-Neira, Ana I., et autres
Publié: (2023)

Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
par: de Vazelhes, William, et autres
Publié: (2023)

Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities
par: Jiang, Wei-Bang, et autres
Publié: (2025)

Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformation
par: Yan, Yi, et autres
Publié: (2023)

AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation
par: Guler, Berkay, et autres
Publié: (2025)

A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
par: van Gorp, Hans, et autres
Publié: (2024)

Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals
par: Toosi, Maedeh H., et autres
Publié: (2026)

Spatially-Adaptive Conformal Graph Transformer for Indoor Localization in Wi-Fi Driven Networks
par: Lehyeh, Ayesh Abu, et autres
Publié: (2026)

Sparse Graph Learning from Sparse Data via Fiedler Number Maximization
par: Oveisgharan, Bahar, et autres
Publié: (2026)

Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation
par: Orfanidis, Georgios I.
Publié: (2026)

Differentiable High-Order Markov Models for Spectrum Prediction
par: Corlay, Vincent, et autres
Publié: (2024)

Ubiquity of Emergent Hebbian Dynamics in Regularized Learning
par: Koplow, David, et autres
Publié: (2025)

The Transferability of Downsamped Sparse Graph Convolutional Networks
par: Shu, Qinji, et autres
Publié: (2024)

Enhancing Off-Grid One-Bit DOA Estimation with Learning-Based Sparse Bayesian Approach for Non-Uniform Sparse Array
par: Hu, Yunqiao, et autres
Publié: (2024)

Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
par: Wang, Xueyi, et autres
Publié: (2025)

Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
par: Wagner, Patrick, et autres
Publié: (2023)

What Can Be Recovered Under Sparse Adversarial Corruption? Assumption-Free Theory for Linear Measurements
par: Halder, Vishal, et autres
Publié: (2025)

Low-Rank Matrix Factorizations with Volume-based Constraints and Regularizations
par: Thanh, Olivier Vu
Publié: (2024)

Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO
par: Momen-Tayefeh, Mehrdad, et autres
Publié: (2026)

Study of Robust Direction Finding Based on Joint Sparse Representation
par: Li, Y., et autres
Publié: (2024)

Explicit Group Sparse Projection with Applications to Deep Learning and NMF
par: Ohib, Riyasat, et autres
Publié: (2019)

Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels
par: Ott, Jonathan, et autres
Publié: (2025)

Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction
par: Mazzieri, Riccardo, et autres
Publié: (2023)

Downlink Channel Covariance Matrix Estimation via Representation Learning with Graph Regularization
par: Zerin, Melih Can, et autres
Publié: (2024)

On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series
par: Tugnait, Jitendra K.
Publié: (2021)

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
par: Jenkins, Alexander, et autres
Publié: (2025)

Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties
par: Tugnait, Jitendra K
Publié: (2025)

Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
par: Giraldo, Jhony H., et autres
Publié: (2024)