IEEE transactions on neural networks and learning systems
Oct 5, 2022
Due to the benefits of reduced maintenance cost and increased operational safety, effective prognostic methods have always been highly demanded in real industries. In the recent years, intelligent data-driven remaining useful life (RUL) prediction ap...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
There are two main categories of face sketch synthesis: data- and model-driven. The data-driven method synthesizes sketches from training photograph-sketch patches at the cost of detail loss. The model-driven method can preserve more details, but the...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
In recommendation, both stationary and dynamic user preferences on items are embedded in the interactions between users and items (e.g., rating or clicking) within their contexts. Sequential recommender systems (SRSs) need to jointly involve such con...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Spike-timing-dependent plasticity (STDP) is one of the most popular and deeply biologically motivated forms of unsupervised Hebbian-type learning. In this article, we propose a variant of STDP extended by an additional activation-dependent scale fact...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Training neural networks with backpropagation (BP) requires a sequential passing of activations and gradients. This has been recognized as the lockings (i.e., the forward, backward, and update lockings) among modules (each module contains a stack of ...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
In the brief, delayed impulsive control is investigated for the synchronization of chaotic neural networks. In order to overcome the difficulty that the delays in impulsive control input can be flexible, we utilize the concept of average impulsive de...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Dropout is a well-known regularization method by sampling a sub-network from a larger deep neural network and training different sub-networks on different subsets of the data. Inspired by the dropout concept, we propose EDropout as an energy-based fr...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article is devoted to investigating finite-time synchronization (FTS) for coupled neural networks (CNNs) with time-varying delays and Markovian jumping topologies by using an intermittent quantized controller. Due to the intermittent property, i...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Model quantization is essential to deploy deep convolutional neural networks (DCNNs) on resource-constrained devices. In this article, we propose a general bitwidth assignment algorithm based on theoretical analysis for efficient layerwise weight and...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Energy consumption is an important issue for resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) is a promising framework for addressing this challenge because it can compress data in an en...