Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

The representation of individual memories in a recurrent neural network can be efficiently differentiated using chaotic recurrent dynamics.

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Introduction: Underwater acoustic (UWA) communication systems confront significant challenges due to the unique, dynamic, and unpredictable nature of acoustic channels, which are impacted by low ...

Abstract: In recent years, bidirectional convolutional recurrent neural networks (RNNs) have made significant breakthroughs in addressing a wide range of challenging problems related to time series ...

Abstract: Shifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in ...

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 ...

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 ...

Autoregressive Transformers have become the leading approach for sequence modeling due to their strong in-context learning and parallelizable training enabled by softmax attention. However, softmax ...