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691 results found.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend PCA to the union of subspace (UoS) setting for clustering d
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
The Kalman Filter (KF) is used to estimate and predict state-space models. It frequently encounters uncertain covariates, such as those derived from noisy measurements of physical signals (e.g., senso
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
This paper investigates the sparse phase retrieval problem, which aims to recover a sparse signal from a system of quadratic measurements. In this work, we propose a novel non-convex algorithm, termed
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Compressive sensing (CS) enables fast spatial channel estimation in millimeter-wave and terahertz systems by leveraging the sparsity of the channel in the angle-domain. CS measurements, however, are o
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
This article investigates the problem of deep learning-based state estimation for dynamic systems with partially known dynamics and unknown noise statistics. We propose a Deep Double-Bayesian Filter (
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Identifying the graphical structure underlying observed multivariate data is essential in numerous applications. Current methodologies are predominantly confined to deducing a single graph under the p
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
The efficacy of logit transfer in knowledge distillation (KD) is often hampered by disparities in model architectures and imbalances in data distributions. Existing decoupled distillation methods pred
Section: Publications & Resources
Deep Reinforcement Learning (DRL) has demonstrated remarkable capabilities in domains such as robotics, finance, and autonomous systems. With the increasing cost of training, DRL models are increasing
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
Multimodal sentiment analysis is vulnerable to unstable local cues and temporal asynchrony among textual, visual, and acoustic signals. Existing fusion methods extensively model cross-modal interactio
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
When deployed on unreliable hardware, deep neural networks (DNNs) encounter weight perturbations that affect model outputs, increasing uncertainty and posing significant risks to the application of de
