AIMC Topic: Learning

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Accommodating Multiple Tasks' Disparities With Distributed Knowledge-Sharing Mechanism.

IEEE transactions on cybernetics Apr 5, 2022
Deep multitask learning (MTL) shares beneficial knowledge across participating tasks, alleviating the impacts of extreme learning conditions on their performances such as the data scarcity problem. In practice, participators stemming from different d...
Neural Networks, Computer Machine Learning Learning Pancreas
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Using Kernel Method to Include Firm Correlation for Stock Price Prediction.

Computational intelligence and neuroscience Apr 5, 2022
In this work, we propose AGKN (attention-based graph learning kernel network), a novel framework to incorporate information of correlated firms of a target stock for its price prediction in an end-to-end way. We first construct a stock-axis attention...
Neural Networks, Computer Learning Investments
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TAHDNet: Time-aware hierarchical dependency network for medication recommendation.

Journal of biomedical informatics Apr 4, 2022
Medication recommendation is a hot topic in the research of applying neural networks to the healthcare area. Although extensive progressions have been made, current researches still face the following challenges: (i). Existing methods are poor at eff...
Humans Neural Networks, Computer Learning
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Subtraction Gates: Another Way to Learn Long-Term Dependencies in Recurrent Neural Networks.

IEEE transactions on neural networks and learning systems Apr 4, 2022
Recurrent neural networks (RNNs) can remember temporal contextual information over various time steps. The well-known gradient vanishing/explosion problem restricts the ability of RNNs to learn long-term dependencies. The gate mechanism is a well-dev...
Neural Networks, Computer Algorithms Learning
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Semicentralized Deep Deterministic Policy Gradient in Cooperative StarCraft Games.

IEEE transactions on neural networks and learning systems Apr 4, 2022
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...
Neural Networks, Computer Algorithms Learning Policy Reward
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BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks.

IEEE transactions on neural networks and learning systems Apr 4, 2022
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...
Neural Networks, Computer Learning Bayes Theorem
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Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain.

Sensors (Basel, Switzerland) Mar 28, 2022
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...
Neural Networks, Computer Machine Learning Learning Intelligence
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Dark Web Data Classification Using Neural Network.

Computational intelligence and neuroscience Mar 28, 2022
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...
Neural Networks, Computer Machine Learning Learning
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Design and Implementation of IoT Data-Driven Intelligent Law Classroom Teaching System.

Computational intelligence and neuroscience Mar 25, 2022
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...
Humans Artificial Intelligence Software Learning Intelligence Curriculum Internet of Things Teaching Jurisprudence
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GHNN: Graph Harmonic Neural Networks for semi-supervised graph-level classification.

Neural networks : the official journal of the International Neural Network Society Mar 24, 2022
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...
Neural Networks, Computer Algorithms Learning Attention
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