A new study published in Scientific Reports has introduced a promising diagnostic tool that could dramatically shorten the long wait times many families face when seeking evaluations for autism and ...

Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

There are many different types of anomaly detection techniques. This article explains how to use a neural autoencoder implemented using raw C# to find anomalous data items. Compared to other anomaly ...

In this paper, we present a deep learning based method for blind hyperspectral unmixing in the form of a neural network autoencoder. We show that the linear mixture model implicitly puts certain ...

Autoencoder networks: the core of the attentional autoencoder network is the autoencoder. An autoencoder is a neural network structure that consists of an encoder and a decoder.

To bridge this gap, in a new paper SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs, a research team from Google Research and Carnegie Mellon University introduces ...