AIMC Topic: Algorithms

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Item Relationship Graph Neural Networks for E-Commerce.

IEEE transactions on neural networks and learning systems
In a modern e-commerce recommender system, it is important to understand the relationships among products. Recognizing product relationships-such as complements or substitutes-accurately is an essential task for generating better recommendation resul...

Joint Label Inference and Discriminant Embedding.

IEEE transactions on neural networks and learning systems
Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requ...

A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks.

IEEE transactions on neural networks and learning systems
It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping int...

Evolutionary Shallowing Deep Neural Networks at Block Levels.

IEEE transactions on neural networks and learning systems
Neural networks have been demonstrated to be trainable even with hundreds of layers, which exhibit remarkable improvement on expressive power and provide significant performance gains in a variety of tasks. However, the prohibitive computational cost...

Multistability and Stabilization of Fractional-Order Competitive Neural Networks With Unbounded Time-Varying Delays.

IEEE transactions on neural networks and learning systems
This article investigates the multistability and stabilization of fractional-order competitive neural networks (FOCNNs) with unbounded time-varying delays. By utilizing the monotone operator, several sufficient conditions of the coexistence of equili...

Perturbation of Spike Timing Benefits Neural Network Performance on Similarity Search.

IEEE transactions on neural networks and learning systems
Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space an...

Capped Linex Metric Twin Support Vector Machine for Robust Classification.

Sensors (Basel, Switzerland)
In this paper, a novel robust loss function is designed, namely, capped linear loss function Laε. Simultaneously, we give some ideal and important properties of Laε, such as boundedness, nonconvexity and robustness. Furthermore, a new binary classifi...

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning.

Sensors (Basel, Switzerland)
In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal act...

Deep Learning Reconstruction Algorithm-Based MRI Image Evaluation of Edaravone in the Treatment of Lower Limb Ischemia-Reperfusion Injury.

Contrast media & molecular imaging
This research aimed to evaluate the therapeutic effect of edaravone on lower limb ischemia-reperfusion injury by MRI images of graph patch-based directional curvelet transform (GPBDCT), compression reconstruction algorithm. 200 patients with lower li...

Task Offloading and Resource Allocation Strategy Based on Deep Learning for Mobile Edge Computing.

Computational intelligence and neuroscience
For the problems of unreasonable computation offloading and uneven resource allocation in Mobile Edge Computing (MEC), this paper proposes a task offloading and resource allocation strategy based on deep learning for MEC. Firstly, in the multiuser mu...