IEEE Transactions on Neural Networks and Learning Systems

Electrocardiography (ECG) signals can be considered as multivariable time series (TS). The state-of-the-art ECG data classification approaches, based on either feature engineering or deep learning techniques, treat separately spectral and time domains in machine learning systems. No spectral-time domain communication mechanism inside the classifier model can be found in current approaches, leading to difficulties in identifying complex ECG forms. In this article, we proposed a novel deep learning model named spectral cross-domain neural network (SCDNN) with a new block called soft-adaptive threshold spectral enhancement (SATSE), to simultaneously reveal the key information embedded in spectral and time domains inside the neural network...

Concept-cognitive learning is an emerging area of cognitive computing, which refers to continuously learning new knowledge by imitating the human cognition process. However, the existing research on concept-cognitive learning is still at the level of complete cognition as well as cognitive operators, which is far from the real cognition process. Meanwhile, the current classification algorithms based on concept-cognitive learning models (CCLMs) are not mature enough yet since their cognitive results highly depend on the cognition order of attributes...

We propose a novel master-slave architecture to solve the top- K combinatorial multiarmed bandits (CMABs) problem with nonlinear bandit feedback and diversity constraints, which, to the best of our knowledge, is the first combinatorial bandits setting considering diversity constraints under bandit feedback. Specifically, to efficiently explore the combinatorial and constrained action space, we introduce six slave models with distinguished merits to generate diversified samples well balancing rewards and constraints as well as efficiency...

The purpose of makeup transfer (MT) is to transfer makeup from a reference image to a target face while preserving the target's content. Existing methods have made remarkable progress in generating realistic results but do not perform well in terms of semantic correspondence and color fidelity. In addition, the straightforward extension of processing videos frame by frame tends to produce flickering results in most methods. These limitations restrict the applicability of previous methods in real-world scenarios...

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph representation learning scenarios. However, when applied to graph data in real world, GNNs have encountered scalability issues. Existing GNNs often have high computational load in both training and inference stages, making them incapable of meeting the performance needs of large-scale scenarios with a large number of nodes. Although several studies on scalable GNNs have developed, they either merely improve GNNs with limited scalability or come at the expense of reduced effectiveness...

Neuromorphic hardware using nonvolatile analog synaptic devices provides promising advantages of reducing energy and time consumption for performing large-scale vector-matrix multiplication (VMM) operations. However, the reported training methods for neuromorphic hardware have appreciably shown reduced accuracy due to the nonideal nature of analog devices, and use conductance tuning protocols that require substantial cost for training. Here, we propose a novel hybrid training method that efficiently trains the neuromorphic hardware using nonvolatile analog memory cells, and experimentally demonstrate the high performance of the method using the fabricated hardware...

Symbolic regression (SR) is the process of finding an unknown mathematical expression given the input and output and has important applications in interpretable machine learning and knowledge discovery. The major difficulty of SR is that finding the expression structure is an NP-hard problem, which makes the entire process time-consuming. In this study, the solution of expression structures was regarded as a classification problem and solved by supervised learning such that SR can be solved quickly by using the solving experience...

Inspired by the diversity of biological neurons, quadratic artificial neurons can play an important role in deep learning models. The type of quadratic neurons of our interest replaces the inner-product operation in the conventional neuron with a quadratic function. Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed. Theoretically, the superior expressivity of a quadratic network over either a conventional network or a conventional network via quadratic activation is not fully elucidated, which makes the use of quadratic networks not well grounded...

This article proposes a new hashing framework named relational consistency induced self-supervised hashing (RCSH) for large-scale image retrieval. To capture the potential semantic structure of data, RCSH explores the relational consistency between data samples in different spaces, which learns reliable data relationships in the latent feature space and then preserves the learned relationships in the Hamming space. The data relationships are uncovered by learning a set of prototypes that group similar data samples in the latent feature space...

Gait recognition has become a mainstream technology for identification, as it can recognize the identity of subjects from a distance without any cooperation. However, when subjects wear coats (CL) or backpacks (BG), their gait silhouette will be occluded, which will lose some gait information and bring great difficulties to the identification. Another important challenge in gait recognition is that the gait silhouette of the same subject captured by different camera angles varies greatly, which will cause the same subject to be misidentified as different individuals under different camera angles...

Information diffusion prediction is a complex task due to the dynamic of information substitution present in large social platforms, such as Weibo and Twitter. This task can be divided into two levels: the macroscopic popularity prediction and the microscopic information diffusion prediction (who is next), which share the essence of modeling the dynamic spread of information. While many researchers have focused on the internal influence of individual cascades, they often overlook other influential factors that affect information diffusion, such as competition and cooperation among information, the attractiveness of information to users, and the potential impact of content anticipation on further diffusion...

Facing large-scale online learning, the reliance on sophisticated model architectures often leads to nonconvex distributed optimization, which is more challenging than convex problems. Online recruited workers, such as mobile phone, laptop, and desktop computers, often have narrower uplink bandwidths than downlink. In this article, we propose two communication-efficient nonconvex federated learning algorithms with error feedback 2021 (EF21) and lazily aggregated gradient (LAG) for adapting uplink and downlink communications...

In this article, we investigate a novel but insufficiently studied issue, unpaired multi-view clustering (UMC), where no paired observed samples exist in multi-view data, and the goal is to leverage the unpaired observed samples in all views for effective joint clustering. Existing methods in incomplete multi-view clustering usually utilize the sample pairing relationship between views to connect the views for joint clustering, but unfortunately, it is invalid for the UMC case. Therefore, we strive to mine a consistent cluster structure between views and propose an effective method, namely selective contrastive learning for UMC (scl-UMC), which needs to solve the following two challenging issues: 1) uncertain clustering structure under no supervision information and 2) uncertain pairing relationship between the clusters of views...

Brain-inspired computing technique presents a promising approach to prompt the rapid development of artificial general intelligence (AGI). As one of the most critical aspects, spiking neural networks (SNNs) have demonstrated superiority for AGI, such as low power consumption. Effective training of SNNs with high generalization ability, high robustness, and low power consumption simultaneously is a significantly challenging problem for the development and success of applications of spike-based machine intelligence...

The score-based generative model (SGM) can generate high-quality samples, which have been successfully adopted for magnetic resonance imaging (MRI) reconstruction. However, the recent SGMs may take thousands of steps to generate a high-quality image. Besides, SGMs neglect to exploit the redundancy in k space. To overcome the above two drawbacks, in this article, we propose a fast and reliable SGM (FRSGM). First, we propose deep ensemble denoisers (DEDs) consisting of SGM and the deep denoiser, which are used to solve the proximal problem of the implicit regularization term...

Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to reduce the complexity cost. However, due to the sampling operation being performed on each individual view independently and not considering the distribution of samples in all views, the produced anchors are usually slightly distinguishable, failing to characterize the whole data. Moreover, it is necessary to fuse multiple separated graphs into one, which leads to the final clustering performance heavily subject to the fusion algorithm adopted...

Drones are set to penetrate society across transport and smart living sectors. While many are amateur drones that pose no malicious intentions, some may carry deadly capability. It is crucial to infer the drone's objective to prevent risk and guarantee safety. In this article, a policy error inverse reinforcement learning (PEIRL) algorithm is proposed to uncover the hidden objective of drones from online data trajectories obtained from cooperative sensors. A set of error-based polynomial features are used to approximate both the value and policy functions...

In recent years, adaptive drive-response synchronization (DRS) of two continuous-time delayed neural networks (NNs) has been investigated extensively. For two timescale-type NNs (TNNs), how to develop adaptive synchronization control schemes and demonstrate rigorously is still an open problem. This article concentrates on adaptive control design for synchronization of TNNs with unbounded time-varying delays. First, timescale-type Barbalat lemma and novel timescale-type inequality techniques are first proposed, which provides us practical methods to investigate timescale-type nonlinear systems...

Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process...

An autonomous underwater vehicle (AUV) has shown impressive potential and promising exploitation prospects in numerous marine missions. Among its various applications, the most essential prerequisite is path planning. Although considerable endeavors have been made, there are several limitations. A complete and realistic ocean simulation environment is critically needed. As most of the existing methods are based on mathematical models, they suffer from a large gap with reality. At the same time, the dynamic and unknown environment places high demands on robustness and generalization...