Results 61 to 70 of about 56,672 (314)
A quantum autoencoder: using machine learning to compress qutrits
, 2020 The compression of quantum data will allow increased control over difficult-to- manage quantum resources. We experimentally realize a quantum autoencoder, which learns to compress quantum data with a classical machine learning routine.Full ...
Geoff J. Pryde +5 more
core +1 more source
The deep kernelized autoencoder [PDF]
Applied Soft Computing, 2018 Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing ...
Michael Kampffmeyer +4 more
openaire +4 more sources
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
Evidence for Itinerant Ferromagnetic Flat Bands Producing Large Transverse Responses
Advanced Materials, EarlyView.Itinerant ferromagnetic flat bands are demonstrated in GdCo5 with a high Curie temperature of 940K, a stacked honeycomb–kagome lattice, through angle‐resolved photoemission spectroscopy and magneto‐thermoelectric measurements. These topological flat bands generate large Berry curvaturte, producing gigantic anomalous Nernst effect with record‐high ...
Susumu Minami +15 more
wiley +1 more source
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
Autoencoder trained on transcriptomic signals
, 2019 (A) Microarray normalised gene expression data used in reverse training to define the disease modules: MicroarrayDataForDiseaseGene.zip(B) 3 layer with 512 nodes in first, second and third layer deep autoencoder trained on the 20K microarray data ...
sanjiv dwivedi (7040567)
core +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Fooling intrusion detection systems using adversarially autoencoder [PDF]
, 2021 Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed to identify network anomalies. Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows, and the ...
Chen, Junjun +17 more
core +1 more source
Remote SensingHigh-resolution satellite images are frequently used to measure horizontal displacements caused by earthquakes, providing valuable insights into rupture behaviors and mechanical properties of seismogenic faults.
Chenglong Li +4 more
doaj +1 more source
Evaluation of Machine Learning Methods for Monitoring the Health of Guyed Towers
Sensors, 2021 This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil’s interconnected electrical system.
Diana Marcela Martinez Ricardo +5 more
doaj +1 more source