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 ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds and ...

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 ...

The Data Science Lab Data Anomaly Detection Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that ...

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 ...