In this paper, we propose a physics-guided neural network to solve the PF problem, with an auxiliary task to rebuild the PF model. By encoding different granularity of Kirchhoff's laws, and system ...

Interestingly, the issues uncovered explain how common heuristics for data-fit neural networks (e.g. initialization) cause training difficulties in this new paradigm of PINNs. Critically, we believe ...

As a consequence, orchestrating these abstractions to implement a common network policy becomes an arduous task. To address this challenge, plain data representations of the network and its control ...

Abstract: Several means for improving the performance and training of neural networks for classification are proposed. Crossvalidation is used as a tool for optimizing network parameters and ...

The proposed model takes a picture of a handwritten text as input and converts it into digital text. The Convolutional Neural Network (CNN) is used to study the features of similar objects from ...

Therefore, we propose an attention-based graph neural network news recommendation model. In our model, muti-channel convolutional neural network is used to generate news representations, and recurrent ...

It now appears that neural nets may be the next frontier in the advance of computing technology as a whole. But what are ...

As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications ...

In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems.

The excellent local feature representation ability of convolutional neural network (CNN) makes it achieve good classification results in hyperspectral image classification tasks, but its ability to ...

Abstract: In this article, we design a memristive competitive neural network circuit based on the winner-take-all (WTA) mechanism and the online Hebbian learning rule. Each synapse of the network ...

The development of deep learning techniques brings the light on this issue. In this work, we propose a hybrid initiative called HCGNet, combining convolutional neural network (CNN) and vision graph ...