AIMC Topic: Learning

Clear Filters Showing 501 to 510 of 1398 articles

ExpGCN: Review-aware Graph Convolution Network for explainable recommendation.

Neural networks : the official journal of the International Neural Network Society
Existing works in recommender system have widely explored extracting reviews as explanations beyond user-item interactions, and formulated the explanation generation as a ranking task to enhance item recommendation performance. To associate explanati...

Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks.

Sensors (Basel, Switzerland)
Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things (IIoT) due to the increase in cyber attack dangers for distributed devices and critical infrastructure networks. To face these challenges, the Intrusion D...

Embedding cognitive framework with self-attention for interpretable knowledge tracing.

Scientific reports
Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to...

Mechanical neural networks: Architected materials that learn behaviors.

Science robotics
Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned beha...

Decentralized Neurocontroller Design With Critic Learning for Nonlinear-Interconnected Systems.

IEEE transactions on cybernetics
We consider the decentralized control problem of a class of continuous-time nonlinear systems with mismatched interconnections. Initially, with the discounted cost functions being introduced to auxiliary subsystems, we have the decentralized control ...

Neural representational geometry underlies few-shot concept learning.

Proceedings of the National Academy of Sciences of the United States of America
Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and...

Multi-Aspect enhanced Graph Neural Networks for recommendation.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have achieved remarkable performance in personalized recommendation, for their powerful data representation capabilities. However, these methods still face several challenging problems: (1) the majority of user-item inter...

Leveraging Expert Demonstration Features for Deep Reinforcement Learning in Floor Cleaning Robot Navigation.

Sensors (Basel, Switzerland)
In this paper, a Deep Reinforcement Learning (DRL)-based approach for learning mobile cleaning robot navigation commands that leverage experience from expert demonstrations is presented. First, expert demonstrations of robot motion trajectories in si...

Adversarial deep evolutionary learning for drug design.

Bio Systems
The design of a new therapeutic agent is a time-consuming and expensive process. The rise of machine intelligence provides a grand opportunity of expeditiously discovering novel drug candidates through smart search in the vast molecular structural sp...

Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set Network.

Computational intelligence and neuroscience
The rationality and timeliness of the comprehensive results of English course learning quality are increasingly important in the process of modern education. There are some problems in the scientific evaluation of English course learning quality and ...