Results 41 to 50 of about 113,519 (298)
Quantum variational autoencoder [PDF]
Quantum Science and Technology, 2018 Variational autoencoders (VAEs) are powerful generative models with the salient ability to perform inference. Here, we introduce a quantum variational autoencoder (QVAE): a VAE whose latent generative process is implemented as a quantum Boltzmann machine (QBM). We show that our model can be trained end-to-end by maximizing a well-defined loss-function:
Amir Khoshaman +5 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.Cuttlebone‐inspired metamaterials exploit a septum‐wall architecture to achieve excellent mechanical and functional properties. This review classifies existing designs into direct biomimetic, honeycomb‐type, and strut‐type architectures, summarizes governing design principles, and presents a decoupled design framework for interpreting multiphysical ...
Xinwei Li, Zhendong Li
wiley +1 more source
MRI-based Surgical Planning for Lumbar Spinal Stenosis
, 2017 The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis(LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon shows large variance.
Andrew J. Haig +12 more
core +1 more source
Symmetric Wasserstein Autoencoders
, 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, July 27-30, 2021, Virtual ...
Sun, Sun, Guo, Hongyu
openaire +3 more sources
Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
EnergiesAnomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies.
Fadhila Lachekhab +4 more
doaj +1 more source
A Hybrid Autoencoder Network for Unsupervised Image Clustering
Algorithms, 2019 Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision.
Pei-Yin Chen, Jih-Jeng Huang
doaj +1 more source
, 2017 Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder.
Feng, Ji, Zhou, Zhi-Hua
core +1 more source
Autoencoding Topographic Factors [PDF]
Journal of Computational Biology, 2019 Topographic factor models separate overlapping signals into latent spatial functions to identify correlation structure across observations. These methods require the underlying structure to be held fixed and are not robust to deviations commonly found across images.
Antonio, Moretti +3 more
openaire +2 more sources
Advanced Materials Technologies, EarlyView.The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz +5 more
wiley +1 more source