Results 41 to 50 of about 93,522 (265)

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, Walter Vinci, Brandon Denis, Evgeny Andriyash, Hossein Sadeghi, Mohammad H Amin   +5 more
openaire   +2 more sources

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

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, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +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

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, Yangming Wang, Seigo Souma, Hiroto Nakamura, Akito Sakai, Takumi Osumi, Hang Su, Hikaru Watanabe, Shun'ichiro Kurosawa, Rikuto Oiwa, Daisuke Nishio‐Hamane, Kosuke Nakayama, Takuya Nomoto, Ryotaro Arita, Takafumi Sato, Satoru Nakatsuji   +15 more
wiley   +1 more source

Efficient Local Image Descriptors Learned With Autoencoders

IEEE Access, 2022
Local image descriptors play a crucial role in many image processing tasks, such as object tracking, object recognition, panorama stitching, and image retrieval.
Nina Zizakic, Aleksandra Pizurica
doaj   +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, Andrew, Stirn, Gabriel, Marks, Itsik, Pe'er   +3 more
openaire   +2 more sources

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

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

Reconstructing Horizontal Displacement Through Deep Learning in Multiple-Pairwise Satellite Image Correlation

Remote Sensing
High-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, Yanxing Wu, Xingyan Wang, Xi Xi, Guohong Zhang   +4 more
doaj   +1 more source

Conditional autoencoder pre-training and optimization algorithms for personalized care of hemophiliac patients

Frontiers in Artificial Intelligence, 2023
This paper presents the use of deep conditional autoencoder to predict the effect of treatments for patients suffering from hemophiliac disorders. Conditional autoencoder is a semi-supervised model that learns an abstract representation of the data and ...
Cédric Buche, Cédric Buche, François Lasson, Sébastien Kerdelo   +3 more
doaj   +1 more source

2D Nanomaterials Toward Function‐Ready Superlubricity in Advanced Microsystems

Advanced Materials, EarlyView.
A unified framework links structural and transformation superlubricity with microsystem functions and deployment requirements. Mechanisms, device architectures, integration strategies, AI‐guided discovery, and benchmarking protocols are connected to define function‐ready superlubricity in advanced microsystems.
Yushan Geng, Jun Yang, Yong Yang
wiley   +1 more source