Results 61 to 70 of about 30,442 (292)

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, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi   +4 more
openaire   +4 more sources

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Study of variational autoencoders in machine learning [PDF]

, 2023
Autoencoders are essential in the field of machine learning because of the wide range of applications and distinctive talents they have. The ability of autoencoders to learn condensed and effective representations of complicated input data is one of the ...
Campo Moyà, Joel
core   +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, 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

DEEP NON-NEGATIVE MATRIX FACTORIZATION MODEL FOR CLUSTERING-BASED IMAGE DENOISING [PDF]

Proceedings on Engineering Sciences
Technologies like self-driving cars and cleaning robots are emerging as mainstream technologies. These technologies make use of cognitive recognition.
Shaily Malik , Nishtha Jatana , Divya Jatain , Geetanjali Sharma , Vaani Garg, Amita Yadav   +5 more
doaj   +1 more source

Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection

IEEE Access
Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods.
Asim Niaz, Sareer Ul Amin, Shafiullah Soomro, Hamza Zia, Kwang Nam Choi   +4 more
doaj   +1 more source

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

Scalable and Efficient Protein Secondary Structure Prediction Using Autoencoder-Reduced ProtBERT Embeddings

Applied Sciences
This study proposes a deep learning framework for Protein Secondary Structure Prediction (PSSP) that prioritizes computational efficiency while preserving classification accuracy.
Yahya Najib Hamood Al-Shameri, İrfan Kösesoy, Hakan Gündüz, Ömer Faruk Yılmaz   +3 more
doaj   +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, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Discriminative Autoencoders for Small Targets Detection [PDF]

, 2014
International audienceThis paper introduces the new concept of discriminative autoencoders. In contrast with the standard autoencoders -- which are artificial neural networks used to learn compressed representation for a set of data -- discriminative ...
Razakarivony, Sebastien, Frederic Jurie, Jurie, Frédéric, Sebastien Razakarivony   +3 more
core   +1 more source