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Article
Open AccessConstraining acyclicity of differentiable Bayesian structure learning with topological ordering
Distributional estimates in Bayesian approaches in structure learning have advantages compared to the ones performing point estimates when handling epistemic uncertainty. Differentiable methods for Bayesian st...
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Chapter
EvSys: A Relational Dynamic System for Sparse Irregular Clinical Events
Clinical events such as clinic visits, hospital admissions, ECG readings and lab tests are recorded in modern healthcare systems. While these offer a great wealth of information about the state of health for a...
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Chapter and Conference Paper
Knowledge Distillation with Distribution Mismatch
Knowledge distillation (KD) is one of the most efficient methods to compress a large deep neural network (called teacher) to a smaller network (called student). Current state-of-the-art KD methods assume that the...
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Chapter and Conference Paper
Fast Conditional Network Compression Using Bayesian HyperNetworks
We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target ...
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Chapter and Conference Paper
Variational Hyper-encoding Networks
We propose a framework called HyperVAE for encoding distributions of distributions. When a target distribution is modeled by a VAE, its neural network parameters are sampled from a distribution in the model sp...
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Article
Gut Microbiota of Migrating Wild Rabbit Fish (Siganus guttatus) Larvae Have Low Spatial and Temporal Variability
We investigated the gut microbiota of rabbit fish larvae at three locations in Vietnam (ThuanAn—northern, QuangNam—intermediate, BinhDinh—southern sampling site) over a three-year period. In the wild, the firs...
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Chapter and Conference Paper
\(\mathsf {vUBM}\): A Variational Universal Background Model for EEG-Based Person Authentication
EEG-based person authentication is an important means for modern biometrics. However EEG signals are well-known for small signal-to-noise ratio and have many factors of variation. These variations are caused b...
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Chapter and Conference Paper
EEG-Based Person Authentication with Variational Universal Background Model
Silent speech is a convenient and natural way for person authentication as users can imagine speaking their password instead of ty** it. However there are inherent noises and complex variations in EEG signal...
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Chapter and Conference Paper
Repulsive-SVDD Classification
Support vector data description (SVDD) is a well-known kernel method that constructs a minimal hypersphere regarded as a data description for a given data set. However SVDD does not take into account any stati...
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Chapter and Conference Paper
EEG-Based User Authentication Using Artifacts
Recently, electroencephalography (EEG) is considered as a new potential type of user authentication with many security advantages of being difficult to fake, impossible to observe or intercept, unique, and ali...
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Article
Proximity multi-sphere support vector clustering
Support vector data description constructs an optimal hypersphere in feature space as a description of a data set. This hypersphere when mapped back to input space becomes a set of contours, and support vector...
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Chapter and Conference Paper
EEG-Based User Authentication in Multilevel Security Systems
User authentication plays an important role in security systems. In general, there are three types of authentications: password based, token based, and biometrics based. Each of them has its own merits and dra...
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Chapter and Conference Paper
A Study on the Feasibility of Using EEG Signals for Authentication Purpose
Authentication is to verify if one is who he/she claims. It plays an important role in security systems. In this paper, we study the feasibility of using Electroencephalography (EEG) brain signals for authenti...
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Chapter and Conference Paper
Motor Imagery EEG-Based Person Verification
We investigate in this paper the activity-dependent person verification method using electroencephalography (EEG) signal from a person performing motor imagery tasks. Two tasks were performed in our experiment...
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Chapter and Conference Paper
High Order Moment Features for NIRS-Based Classification Problems
This paper aims to experiment high order moment features in two well-known problems which are motor imagery and person authentication in Brain Computer Interface (BCI) systems using Near Infrared Spectroscopy ...
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Chapter and Conference Paper
EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features
Extracting age and gender information from EEG data has not been investigated. This information is useful in building automatic systems that can classify a person into gender or age groups based on EEG charact...
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Chapter and Conference Paper
EEG-Based Person Verification Using Multi-Sphere SVDD and UBM
The use of brain-wave patterns extracted from electroencephalography (EEG) brain signals for person verification has been investigated recently. The challenge is that the EEG signals are noisy due to low condu...
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Chapter and Conference Paper
Parallel Support Vector Data Description
This paper proposes an extension of Support Vector Data Description (SVDD) to provide a better data description. The extension is called Distant SVDD (DSVDD) that determines a smallest hypersphere enclosing al...
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Chapter and Conference Paper
Experiments on Synchronous Nonlinear Features for 2-Class NIRS-Based Motor Imagery Problem
This paper aims to experiment several synchronous nonlinear features in the well-known 2-class motor imagery problem in Brain Computer Interface (BCI) systems using Near Infrared Spectroscopy (NIRS) technique....
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Chapter and Conference Paper
Multi-Sphere Support Vector Clustering
Current support vector clustering method determines the smallest sphere that encloses the image of a dataset in feature space. This sphere when mapped back to data space will form a set of contours that can be...
