Researchers reveal how modeling the human brain’s hidden wiring could push AI beyond its current limits into human-like ...

Therefore, the objective of this paper is to propose a neural network hardware implementation to be used in deep learning applications. The implementation was developed on a field-programmable gate ...

Figure 1. Basic Autoencoder architecture, showing encoder and decoder components [22]. Figure 2. AE-based framework for signal reconstruction, highlighting latent space compression. Recent ...

Network security is a growing concern as digital infrastructure expands, and traditional measures struggle against modern cyber threats. With the increasing complexity of attacks, there is a need for ...

python deep-learning tensorflow keras autoencoder theory image-denoising autoencoder-architecture autoencoder-neural-network image-clean Updated on Jul 21, 2020 Jupyter Notebook ...

Matlab-AE_MVTS Generic Deep Autoencoder for Time-Series This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each ...