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Semicentralized Deep Deterministic Policy Gradient in Cooperative StarCraft Games.

IEEE transactions on neural networks and learning systems
In this article, we propose a novel semicentralized deep deterministic policy gradient (SCDDPG) algorithm for cooperative multiagent games. Specifically, we design a two-level actor-critic structure to help the agents with interactions and cooperatio...

BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks.

IEEE transactions on neural networks and learning systems
Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood fun...

Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain.

Sensors (Basel, Switzerland)
Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the trainin...

Dark Web Data Classification Using Neural Network.

Computational intelligence and neuroscience
There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online sh...

Design and Implementation of IoT Data-Driven Intelligent Law Classroom Teaching System.

Computational intelligence and neuroscience
In this paper, we conduct in-depth research and analysis by building an IoT data-driven intelligent law classroom teaching system and implementing it in the actual teaching process. Firstly, the application requirements and main functions of the clas...

GHNN: Graph Harmonic Neural Networks for semi-supervised graph-level classification.

Neural networks : the official journal of the International Neural Network Society
Graph classification aims to predict the property of the whole graph, which has attracted growing attention in the graph learning community. This problem has been extensively studied in the literature of both graph convolutional networks and graph ke...

Fast protein structure comparison through effective representation learning with contrastive graph neural networks.

PLoS computational biology
Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein...

CircuitBot: Learning to survive with robotic circuit drawing.

PloS one
Robots with the ability to actively acquire power from surroundings will be greatly beneficial for long-term autonomy and to survive in uncertain environments. In this work, a scenario is presented where a robot has limited energy, and the only way t...

Exploration in neo-Hebbian reinforcement learning: Computational approaches to the exploration-exploitation balance with bio-inspired neural networks.

Neural networks : the official journal of the International Neural Network Society
Recent theoretical and experimental works have connected Hebbian plasticity with the reinforcement learning (RL) paradigm, producing a class of trial-and-error learning in artificial neural networks known as neo-Hebbian plasticity. Inspired by the ro...

Instance importance-Aware graph convolutional network for 3D medical diagnosis.

Medical image analysis
Automatic diagnosis of 3D medical data is a significant goal of intelligent healthcare. By exploiting the abundant pathological information of 3D data, human experts and algorithms can provide accurate predictions for patients. Considering the high c...