IEEE transactions on neural networks and learning systems
Aug 7, 2014
Finding the optimal solution to the constrained l0 -norm minimization problems in the recovery of compressive sensed signals is an NP-hard problem and it usually requires intractable combinatorial searching operations for getting the global optimal s...
IEEE transactions on neural networks and learning systems
Aug 6, 2014
This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by ne...
Journal of neural transmission (Vienna, Austria : 1996)
Jul 19, 2014
While neuroimaging research has advanced our knowledge about fear circuitry dysfunctions in anxiety disorders, findings based on diagnostic groups do not translate into diagnostic value for the individual patient. Machine-learning generates predictiv...
INTRODUCTION: Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percen...
MOTIVATION: Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models, such as variational autoencoders, which us...
This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A MOB pairwise decomposition ...
Mathematical biosciences and engineering : MBE
Jul 27, 2022
With the unprecedented development of big data, it is becoming hard to get the valuable information hence, the recommendation system is becoming more and more popular. When the limited Boltzmann machine is used for collaborative filtering, only the s...
We develop a general framework for statistical inference with the 1-Wasserstein distance. Recently, the Wasserstein distance has attracted considerable attention and has been widely applied to various machine learning tasks because of its excellent p...
Criticality is deeply related to optimal computational capacity. The lack of a renormalized theory of critical brain dynamics, however, so far limits insights into this form of biological information processing to mean-field results. These methods ne...
With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationa...