AIMC Topic: Learning

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Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structured data and achieved better task performance than conventional methods. The foundation of GNNs is the message passing procedure, which propagates the i...

Environmental sound classification using temporal-frequency attention based convolutional neural network.

Scientific reports
Environmental sound classification is one of the important issues in the audio recognition field. Compared with structured sounds such as speech and music, the time-frequency structure of environmental sounds is more complicated. In order to learn ti...

On learning disentangled representations for individual treatment effect estimation.

Journal of biomedical informatics
OBJECTIVE: Estimating the individualized treatment effect (ITE) from observational data is a challenging task due to selection bias, which results from the distributional discrepancy between different treatment groups caused by the dependence between...

Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation.

Neural networks : the official journal of the International Neural Network Society
Multi-view clustering has become an active topic in artificial intelligence. Yet, similar investigation for graph-structured data clustering has been absent so far. To fill this gap, we present a Multi-View Graph embedding Clustering network (MVGC). ...

GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users.

Sensors (Basel, Switzerland)
Gas supply system risk assessment is a serious and important problem in cities. Existing methods tend to manually build mathematical models to predict risk value from single-modal information, i.e., pipeline parameters. In this paper, we attempt to c...

Brain oscillatory correlates of visuomotor adaptive learning.

NeuroImage
Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learn...

Approximation capabilities of neural networks on unbounded domains.

Neural networks : the official journal of the International Neural Network Society
There is limited study in the literature on the representability of neural networks on unbounded domains. For some application areas, results in this direction provide additional value in the design of learning systems. Motivated by an old option pri...

Probabilistic generative modeling and reinforcement learning extract the intrinsic features of animal behavior.

Neural networks : the official journal of the International Neural Network Society
It is one of the ultimate goals of ethology to understand the generative process of animal behavior, and the ability to reproduce and control behavior is an important step in this field. However, it is not easy to achieve this goal in systems with co...

Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

PloS one
Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate th...

A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation.

Computational intelligence and neuroscience
Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation ba...