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Found 29 papers, 8 papers with code
Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors
The problem of recovering a signal $\boldsymbol{x} \in \mathbb{R}^n$ from a quadratic system $\{y_i=\boldsymbol{x}^\top\boldsymbol{A}_i\boldsymbol{x},\ i=1,\ldots, m\}$ with full-rank matrices $\boldsymbol{A}_i$ frequently arises in applications such as unassigned distance geometry and sub-wavelength imaging.
AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception
Driver distraction has become a significant cause of severe traffic accidents over the past decade.
Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns
Purpose: To achieve automatic hyperparameter estimation for the joint recovery of quantitative MR images, we propose a Bayesian formulation of the reconstruction problem that incorporates the signal model.
Context De-confounded Emotion Recognition
However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios.
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors.
Robust Quantitative Susceptibility Mapping via Approximate Message Passing with Parameter Estimation
Purpose: For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion.
Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive.
Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography
Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations.
Approximate Message Passing with Parameter Estimation for Heavily Quantized Measurements
In AMP, the signal of interest is assumed to follow certain prior distribution with unknown parameters.
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
At its core is an implicit variational distribution on binary gates that are dependent on previous observations, which will select the next subset of features to observe.
A Probabilistic Bayesian Approach to Recover $R_2^*$ map and Phase Images for Quantitative Susceptibility Mapping
In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover $R_2^*$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data.
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition
In many machine learning tasks, input features with varying degrees of predictive capability are usually acquired at some cost.
Fast Nonconvex $T_2^*$ Mapping Using ADMM
Magnetic resonance (MR)-$T_2^*$ mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise mapping of desired contrast in the tissue.
Bayesian Massive MIMO Channel Estimation with Parameter Estimation Using Low-Resolution ADCs
The proposed approach leads to a much simpler parameter estimation method, allowing us to work with the quantization noise model directly.
Quantization
Information Theory
Signal Processing
Information Theory
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.
UQ-CHI: An Uncertainty Quantification-Based Contemporaneous Health Index for Degenerative Disease Monitoring
In this paper, we aim at filling this gap by developing an uncertainty quantification based contemporaneous longitudinal index, named UQ-CHI, with a particular focus on continuous patient monitoring of degenerative conditions.
Solving Complex Quadratic Systems with Full-Rank Random Matrices
We tackle the problem of recovering a complex signal $\boldsymbol x\in\mathbb{C}^n$ from quadratic measurements of the form $y_i=\boldsymbol x^*\boldsymbol A_i\boldsymbol x$, where $\boldsymbol A_i$ is a full-rank, complex random measurement matrix whose entries are generated from a rotation-invariant sub-Gaussian distribution.
Information Theory Information Theory
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring.
Geometric Invariants for Sparse Unknown View Tomography
For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances.
Safe Active Feature Selection for Sparse Learning
More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem.
Reconstructing Point Sets from Distance Distributions
Our method is the first practical approach to solve the large-scale noisy beltway problem where the points lie on a loop.
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples.
Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion
We present our preliminary work to determine if patient's vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8).
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach
Emotion recognition from facial expressions is tremendously useful, especially when coupled with smart devices and wireless multimedia applications.
Prognostics of Surgical Site Infections using Dynamic Health Data
Since existing prediction models of SSI have quite limited capacity to utilize the evolving clinical data, we develop the corresponding solution to equip these mHealth tools with decision-making capabilities for SSI prediction with a seamless assembly of several machine learning models to tackle the analytic challenges arising from the spatial-temporal data.
Stacked Approximated Regression Machine: A Simple Deep Learning Approach
With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach".
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity
In this paper, we proposed a general bilevel exclusive sparsity formulation to pursue the diversity by restricting the overall sparsity and the sparsity in each group.
Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis
This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone.
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.
