Dimensionality reduction is a technique that reduces the number of features in a dataset without losing much information. It can help improve the performance and efficiency of machine learning ...

An artificial neural network called an autoencoder is used to learn effective codings ... a straightforward convolutional neural network. LeNet5’s architecture is relatively simple. Image features ...

Built on top of: F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs," IEEE Trans. Signal ...

This article will endeavor to demystify autoencoders, explaining the architecture of autoencoders and their applications. Autoencoders are neural networks. Neural networks are composed of multiple ...

Person Segmentation using custom Autoencoder architecture and evaluation using IoU and Dice metrics, will also include Unet architecture in the future.

In addition, we have designed VLSI architect ure for the proposed CS-DAE neural network to accelerate low hardware cost and less computation. The TUL PYNQTM-Z2 development platform runs the Verilog ...