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Manifold-based Shapley for SAR Recognization Network Explanation
Explainable artificial intelligence (XAI) holds immense significance in enhancing the deep neural network's transparency and credibility, particularly in some risky and high-cost scenarios, like synthetic aperture radar (SAR).
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
SAR Despeckling via Regional Denoising Diffusion Probabilistic Model
Speckle noise poses a significant challenge in maintaining the quality of synthetic aperture radar (SAR) images, so SAR despeckling techniques have drawn increasing attention.
Eigenvalues of Symmetric Non-normalized Discrete Trigonometric Transforms
New explicit analytic expressions for the eigenvalues, together with their multiplicities, for the cases of three DTT (DCT$_{(1)}$, DCT$_{(5)}$, and DST$_{(8)}$), are the main contribution of this paper.
Cluster-CAM: Cluster-Weighted Visual Interpretation of CNNs' Decision in Image Classification
Furthermore, we propose an artful strategy to forge a cognition-base map and cognition-scissors from clustered feature maps.
Fair and skill-diverse student group formation via constrained k-way graph partitioning
Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration.
Demystifying CNNs for Images by Matched Filters
The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era.
VS-CAM: Vertex Semantic Class Activation Mapping to Interpret Vision Graph Neural Network
For standard convolutional neural networks (CNNs), class activation mapping (CAM) methods are commonly used to visualize the connection between CNN's decision and image region by generating a heatmap.
Analytical Interpretation of Latent Codes in InfoGAN with SAR Images
In this paper, we show that latent codes are disentangled to affect the properties of SAR images in a non-linear manner.
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial
To help demystify CNNs, we revisit their operation from first principles and a matched filtering perspective.
Understanding the Basis of Graph Convolutional Neural Networks via an Intuitive Matched Filtering Approach
Graph Convolutional Neural Networks (GCNN) are becoming a preferred model for data processing on irregular domains, yet their analysis and principles of operation are rarely examined due to the black box nature of NNs.
Dynamic Portfolio Cuts: A Spectral Approach to Graph-Theoretic Diversification
Stock market returns are typically analyzed using standard regression, yet they reside on irregular domains which is a natural scenario for graph signal processing.
Improved Coherence Index-Based Bound in Compressive Sensing
Within the Compressive Sensing (CS) paradigm, sparse signals can be reconstructed based on a reduced set of measurements.
Nonstationary Portfolios: Diversification in the Spectral Domain
Classical portfolio optimization methods typically determine an optimal capital allocation through the implicit, yet critical, assumption of statistical time-invariance.
A Probabilistic Spectral Analysis of Multivariate Real-Valued Nonstationary Signals
A class of multivariate spectral representations for real-valued nonstationary random variables is introduced, which is characterised by a general complex Gaussian distribution.
Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution.
Portfolio Cuts: A Graph-Theoretic Framework to Diversification
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure.
