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Group Equivariant Sparse Coding
We describe a sparse coding model of visual cortex that encodes image transformations in an equivariant and hierarchical manner. The model consists...
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A3R-Net: adaptive attention aggregation residual network for sparse DOA estimation
In this paper, a unified deep learning framework is developed for high-precision direction-of-arrival (DOA) estimation. Unlike previous methods that...
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Bayesian optimization of histogram of oriented gradients (HOG) parameters for facial recognition
Facial recognition is a rapidly growing field with applications in security, surveillance, and human-computer interaction. The performance of the...
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Bayesian nonnegative matrix factorization in an incremental manner for data representation
Nonnegative matrix factorization (NMF) is a novel paradigm for feature representation and dimensionality reduction. However, the performance of the...
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A Bayesian sampling framework for asymmetric generalized Gaussian mixture models learning
This paper proposes an effective unsupervised Bayesian framework for learning a finite mixture of asymmetric generalized Gaussian distributions...
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A Multi-Granularity Information-Based Method for Learning High-Dimensional Bayesian Network Structures
The purpose of structure learning is to construct a qualitative relationship of Bayesian networks. Bayesian network with interpretability and...
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An extended sparse model for blind image deblurring
Blind image deblurring is a classical ill-posed problem that usually requires constraints on the clean image, the blur kernel, and noise to make it...
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Spatio-temporal wind speed forecasting with approximate Bayesian uncertainty quantification
The prediction of short- and long-term wind speed has great utility for the industry, especially for wind energy generation. Deep neural networks can...
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Bayesian contiguity constrained clustering
Clustering is a well-known and studied problem, one of its variants, called contiguity-constrained clustering, accepts as a second input a graph used...
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Performance evaluation of pan-sharpening and dictionary learning methods for sparse representation of hyperspectral super-resolution
Because it contains high spectral information, hyperspectral imagery has been used in many areas. However, hyperspectral imagery has low spatial...
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A Bayesian robust CP decomposition approach for missing traffic data imputation
The inevitable problem of missing data is ubiquitous in the real transportation system, which makes the data-driven intelligent transportation system...
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Discriminative Noise Robust Sparse Orthogonal Label Regression-Based Domain Adaptation
Domain adaptation ( DA ) aims to enable a learning model trained from a source domain to generalize well on a target domain, despite the mismatch of...
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Empowering Interpretable, Explainable Machine Learning Using Bayesian Network Classifiers
Even before the deep learning era, the machine learning (ML) community commonly believed that while decision trees, neural networks (NNs), support...
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Gene expression model inference from snapshot RNA data using Bayesian non-parametrics
Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional...
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Bayesian inference of transition matrices from incomplete graph data with a topological prior
Many network analysis and graph learning techniques are based on discrete- or continuous-time models of random walks. To apply these methods, it is...
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Sparse Spectrum Gaussian Process for Bayesian Optimization
We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization (BO). Whilst the current sparse...
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Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing
Orthogonal time–frequency space (OTFS) is a new modulation technique proposed in recent years for high Doppler wireless scenes. To solve the...
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Interpretable Bayesian network abstraction for dimension reduction
Dimension reduction methods is effective for tackling the complexity of models learning from high-dimensional data. Usually, they are presented as a...
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Uncertainty quantification and reliability analysis by an adaptive sparse Bayesian inference based PCE model
An adaptive Bayesian polynomial chaos expansion (BPCE) is developed in this paper for uncertainty quantification (UQ) and reliability analysis. The...
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Bayesian neural hawkes process for event uncertainty prediction
Event data consisting of time of occurrence of the events arises in several real-world applications. A commonly used framework to model such events...
