Neural Processing Letters

Deep convolutional neural network (DCNN) is a kind of hierarchical neural network models and attracts attention in recent years since it has shown high classification performance. DCNN can acquire the feature representation which is a parameter ...

This study analyzes the characteristics of unsupervised feature learning using a convolutional neural network (CNN) to investigate its efficiency for multi-task classification and compare it to supervised learning features. We keep the conventional CNN ...

The concept of deep dictionary learning (DDL) has been recently proposed. Unlike shallow dictionary learning which learns single level of dictionary to represent the data, it uses multiple layers of dictionaries. So far, the problem could only be solved ...

For computing weights of deep neural networks (DNNs), the backpropagation (BP) method has been widely used as a de-facto standard algorithm. Since the BP method is based on a stochastic gradient descent method using derivatives of objective functions, ...

Improving the performance on data representation of an auto-encoder could help to obtain a satisfying deep network. One of the strategies to enhance the performance is to incorporate sparsity into an auto-encoder. Fortunately, sparsity for the auto-...

How to design and train increasingly large neural network models is a topic that has been actively researched for several years. However, while there exists a large number of studies on training deeper and/or wider models, there is relatively little ...

Object tracking is one of the most important processes for object recognition in the field of computer vision. The aim is to find accurately a target object in every frame of a video sequence. In this paper we propose a combination technique of two ...

Hashing is a common solution for content-based multimedia retrieval by encoding high-dimensional feature vectors into short binary codes. Previous works mainly focus on image hashing problem. However, these methods can not be directly used for video ...

This paper proposes model-free deep inverse reinforcement learning to find nonlinear reward function structures. We formulate inverse reinforcement learning as a problem of density ratio estimation, and show that the log of the ratio between an optimal ...

The aim of Content-based Image Retrieval (CBIR) is to find a set of images that best match the query based on visual features. Most existing CBIR systems find similar images in low level features, while Text-based Image Retrieval (TBIR) systems find ...

We propose two-stage null-steering direction-of-arrival (DoA) estimation of ultra wideband (UWB) signals with power inversion algorithm and complex spatio-temporal neural network (CVSTNN). This method can estimate DoA more accurately than conventional ...

We study the Z(2) gauge-invariant neural network which is defined on a partially connected random network and involves Z(2) neuron variables $$S_i$$Si ($$=\pm $$= 1) and Z(2) synaptic connection (gauge) variables $$J_{ij}$$Jij ($$=\pm $$= 1). Its energy ...

This paper presents the deduction of the enhanced gradient descent, conjugate gradient, scaled conjugate gradient, quasi-Newton, and Levenberg---Marquardt methods for training quaternion-valued feedforward neural networks, using the framework of the HR ...

Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which mines user check-in sequences to suggest interesting locations for users. Because user check-in behavior exhibits strong temporal ...

The brain can be viewed as a complex modular structure with features of information processing through knowledge storage and retrieval. Modularity ensures that the knowledge is stored in a manner where any complications in certain modules do not affect ...

The aim of this work is to develop a visualization method for multilevel---multigroup analysis based on a multiway nonlinear dimensionality reduction. The task of the method is to visualize what kinds of members each group is composed and to visualize ...

Revealing the neuronal mechanisms enabling the hippocampus to maintain episodic memory (i.e., memory for personal events) is a fundamental issue for our understanding of human intelligence. A temporal compression mechanism based on theta phase coding ...

Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementations matches other digital algorithms on a series of benchmark tasks. Their potential can be further increased by ...

Considering the complicated monotonously increasing character of the displacement series in underground engineering, the original displacement sequence is divided into two components: the displacement trend sequence and the displacement deviation ...

This paper provides finite-time and fixed-time stabilization control strategy for delayed memristive neural networks. Considering that the parameters in the memristive model are state-dependent, which may contain unexpected parameter mismatch when ...

In this paper, the passivity analysis of stochastic memristor-based complex-valued recurrent neural networks (SMCVRNNs) with discrete and distributed time-varying delays is conducted. We adopt a switched system to describe the SMCVRNN with mixed time-...

This article investigates the passivity of reaction---diffusion genetic regulatory networks (GRNs) with time-varying delays and uncertainty terms under Dirichlet, Neumann, and Robin boundary conditions. We provide delay-dependent stability criteria by ...

Single multiplicative neuron model and multilayer perceptron have been commonly used for time series prediction problem. Having a simple structure and features of easily applicable differentiates the single multiplicative neuron model from the ...

In the field of manifold learning, Marginal Fisher Analysis (MFA), Discriminant Neighborhood Embedding (DNE) and Double Adjacency Graph-based DNE (DAG-DNE) construct the graph embedding for homogeneous and heterogeneous k-nearest neighbors (i.e. double ...

This paper is concerned with global robust exponential stability for a class of delayed cellular neural networks with time-varying delays. Some new sufficient conditions are presented for the uniqueness of equilibrium point and the global stability of ...

Discriminant analysis technique plays an important role in face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single image per person ...

This paper is concerned with the stability problem for a class of decentralized event-triggered exponential stability for uncertain delayed genetic regulatory networks (GRNs) with Markov jump parameters and distributed delays. In order to reduce the ...

Kinship verification via facial images is a relatively new and challenging problem in computer vision. Prior studies in the literature have focused solely on gender-fixed kin relation, i.e., on the question of whether one gender-fixed kin relationship ...

In this paper, a new learning algorithm is proposed for complex-valued feedforward neural networks (CVFNNs). The basic idea of this algorithm is that the descent directions of the cost function with respect to complex-valued parameters are calculated by ...