24 accepted papers
Indoor monocular semantic scene completion (MSSC) is notably more challenging than its outdoor counterpart due to complex spatial layouts and severe occlusions. While transformers are well suited for modeling global dependencies, their high memory cost and difficulty in reconstructing fine-grained d
Covariance matrix estimation in high dimensions is a fundamental problem in machine learning and signal processing. A common structural assumption used to mitigate the challenges posed by high dimensionality is sparsity, which posits that most variable pairs exhibit negligible correlations. In this
Estimating statistical parameters from quantized data has received significant attention in recent years, as recovering information from quantized measurements has numerous applications across signal processing, communications, and data analysis. In this work, we focus on maximum likelihood (ML) est…
The generalized hyperbolic (GH) distribution is a highly flexible probability distribution that finds applications in various fields, yet estimating its parameters is quite challenging. This paper focuses on the maximum likelihood (ML) estimation of the GH distribution. In the literature, several ex…
Covariance matrix estimation is a fundamental problem in multivariate data analysis, which becomes particularly challenging in high-dimensional settings due to the curse of dimensionality. To enhance estimation accuracy, structural regularization is often imposed on the precision matrix (the inverse…
The emergence of multi-dimensional data presents significant challenges for traditional regression models based on matrices or vectors, particularly in capturing multi-directional correlations. In response, tensor regression has been proposed as a powerful framework for modeling linear relationships…
Covariance matrix estimation is a fundamental problem in multivariate data analysis. In many situations, it is often observed that variables exhibit a positive linear dependency, indicating a positive linear correlation. This paper tackles the challenge of estimating covariance matrices with positi…
This paper addresses the problem of covariance matrix estimation in scenarios where the underlying variables can be divided into groups, with variables within each group being highly correlated. Consequently, the covariance matrix displays both sparse and approximately low-rank characteristics due t…
Monocular Semantic Scene Completion (MSSC) aims to predict the voxel-wise occupancy and semantic category from a single-view RGB image. Existing methods adopt a single-stage framework that aims to simultaneously achieve visible region segmentation and occluded region hallucination, while also being…
In this paper, we study the sparse principal component analysis (PCA) problem in high-dimensional settings. We propose a novel row-sparse principal subspace estimator, which estimates the subspace spanned by multiple eigenvectors based on the variance maximization problem with a nonconvex sparsity r…
We present an accelerated method for the asymmetric low-rank matrix sensing problem in the over-parameterized setup, named preconditioned gradient descent. We analyze the local convergence rate of the proposed algorithm starting from spectral initialization. Our algorithm is shown to have linear con…
The problem of joint blind deconvolution and demixing for sparse signals is prevalent in many signal processing areas. The goal of this problem is to recover both the sparse signals and the filters from a noisy mixture of bilinear measurements. Due to the bilinear factorization structure, the common…
In this paper, we study the problem of large covariance matrix estimation based on the factor model assumption, in which case the covariance matrix is represented by a combination of a low-rank matrix and a sparse matrix. We formulate the estimation problem as a nonconvex problem, and an iterative o…
Weighted minimum mean squared error (WMMSE) and fractional programming (FP) constitute two common approaches to the weighted sum-rate maximization in communication system design. One subtle issue with WMMSE and FP lies in the tuning of a Lagrange multiplier for the power constraint when it comes to…
The goal of Speech Emotion Recognition (SER) is to enable computers to recognize the emotion category of a given utterance in the same way that humans do. The accuracy of SER is strongly dependent on the validity of the utterance-level representation obtained by the model. Nevertheless, the "dark kn…
The ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> penalized covariance estimator has been widely used for estimating large sparse covariance matrices. It was recognized that ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="…
Depression is a serious mood disorder which brings negative effects on people's social activities. Therefore, growing attention has been paid to automatic depression assessment, especially from speech. However, most of the previous work uses hand-crafted features or deep neural network-based feature…
Automated classification of respiratory sounds has become an active research area in recent years. While recent studies have utilised deep learning methods to aid with respiratory sound classification, the performance is heavily influenced by the datasets available for respiratory sound classificati…
EEG-based emotion recognition is an effective way to infer the inner emotional state of human beings. Recently, deep learning methods, particularly long short-term memory recurrent neural networks (LSTM-RNNs), have made encouraging progress for in the field of emotion recognition. However, the LSTM-…
Normalized difference vegetation index (NDVI) is widely utilized to examine vegetation coverage and estimate crop yield. To obtain a high-resolution (HR) NDVI, fusion techniques, which first generates a HR multispectral (MS) image by fusing a low-resolution (LR) MS image and a HR panchromatic image,…
A growing area of mental health research is the search for speech-based objective markers for conditions such as depression. However, when combined with machine learning, this search can be challenging due to a limited amount of annotated training data. In this paper, we propose a novel crosstask ap…
In this paper, a gradient-based method for bound constrained non-convex problems is proposed. By leveraging both projected gradient descent and perturbed gradient descent, the proposed algorithm, named perturbed projected gradient descent (PP-GD), converges to some approximate second-order stationar…
Minimax multiple-input multiple-output (MIMO) transmit beampattern matching is a fundamental and important problem in many MIMO systems. The problem is formulated to minimize the maximum beampattern matching error as well as suppress the cross-correlation beampatterns while taking different practica…
In this paper, the multiple-input multiple-output (MIMO) transmit beampattern matching problem is considered. The problem is formulated to approximate a desired transmit beampattern (i.e., an energy distribution in space and frequency) and to minimize the cross-correlation of signals reflected back…