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Network in Network (NiN) Explained – Deep Neural Network Tutorial with PyTorch

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
eLife

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...
IEEE

Quantum Gated Recurrent Neural Networks

Abstract: The exploration of quantum advantages with Quantum Neural Networks (QNNs) is an exciting endeavor. Recurrent neural networks, the widely used framework in deep learning, suffer from the ...
Scientific Research Publishing

Chung, J., Gulcehre, C., Cho, K. and Bengio, Y. (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...
Scientific Research Publishing

Medsker, L.R. and Jain, L. (2001) Recurrent Neural Networks: Design and Applications. CRC Press.

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...
GitHub

003 Univariate Model using Recurrent Neural Networks.ipynb

"from sklearn.metrics import mean_absolute_error, mean_squared_error # For measuring model performance / errors\n", "from sklearn.preprocessing import MinMaxScaler # ...