Abstract: Efficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the ...
Deep Learning Crash Course: A Hands-On, Project-Based Introduction to Artificial Intelligence is written by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, ...
NIF is a mesh-agnostic dimensionality reduction paradigm for parametric spatial temporal fields. For decades, dimensionality reduction (e.g., proper orthogonal decomposition, convolutional ...
Amanta NIDS is a real-time Network Intrusion Detection System (NIDS) that leverages Machine Learning to identify and classify network attacks. It integrates NFStream for deep packet inspection and a ...
Variational autoencoders (VAEs) are a powerful class of generative models that can learn to produce realistic and diverse samples of data, such as images, text, or audio. In this tutorial, you will ...
A new Ph.D. program in the physics department designed for students who want to be at the forefront of cosmic discovery, data science and interdisciplinary research. The Mellon College of Science is ...
Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...
Abstract: The analysis of deforming 3D surface meshes is accelerated by autoencoders since the low-dimensional embeddings can be used to visualize underlying dynamics. But, state-of-the-art mesh ...
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