Results 101 to 110 of about 546,017 (306)

Molecularly Engineered Highly Stable Memristors with Ultra‐Low Operational Voltage: Integrating Synthetic DNA with Quasi‐2D Perovskites

Advanced Functional Materials, EarlyView.
Molecularly engineered memristors integrating Ag nanoparticle–embedded synthetic DNA with quasi‐2D halide perovskites enable ultra‐low‐operational voltage, forming‐free resistive switching, and record‐low power density. This synergistic integration of customized DNA and 2D OHP in bio‐hybrid architecture enhances charge transport, reduces variability ...
Kavya S. Keremane, Abhinav Gorthy, Luyao Zheng, Chiranth C Ravi, Haodong Wu, Jiamao Zheng, Neela H. Yennawar, Shashank Priya, Rashmi Jha, Bed Poudel   +9 more
wiley   +1 more source

End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge [PDF]

, 2022
Shakeel A. Sheikh, Md Sahidullah, Fabrice Hirsch, Slim Ouni   +3 more
openalex   +1 more source

Variational Self-Supervised Learning

We present Variational Self-Supervised Learning (VSSL), a novel framework that combines variational inference with self-supervised learning to enable efficient, decoder-free representation learning. Unlike traditional VAEs that rely on input reconstruction via a decoder, VSSL symmetrically couples two encoders with Gaussian outputs.
Yavuz, Mehmet Can, Yanikoglu, Berrin
openaire   +2 more sources

Learning with multimodal self-supervision

, 2021
Deep learning has fueled an explosion of applications, yet training deep neural networks usually requires expensive human annotations. In this thesis we explore alternatives to avoid the substantial reliance on manual annotated examples when training deep neural networks.
openaire   +2 more sources

Mixture of Self-Supervised Learning

, 2023
Self-supervised learning is popular method because of its ability to learn features in images without using its labels and is able to overcome limited labeled datasets used in supervised learning. Self-supervised learning works by using a pretext task which will be trained on the model before being applied to a specific task. There are some examples of
Ruslim, Aristo Renaldo, Yudistira, Novanto, Setiawan, Budi Darma   +2 more
openaire   +2 more sources

From Bug to Feature: Harnessing Cross‐Sensitivity for Multiparametric Luminescence Sensing

Advanced Functional Materials, EarlyView.
Cross‐sensitivity in luminescence sensing is reframed from a limitation into a resource for multiparametric detection. Using ruby microspheres as a model system, cross‐sensitivity is quantitatively assessed and exploited through linear discriminant analysis, enabling simultaneous, correction‐free pressure and temperature sensing with a single ...
Nikita Panov, Liyan Ming, Stanislav Sinogeikin, Erving Ximendes, Daniel Jaque, Riccardo Marin   +5 more
wiley   +1 more source

Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis

Big Data and Cognitive Computing
Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge.
Elie Neghawi, Yan Liu
doaj   +1 more source

Self-Supervised Fast Adaptation for Denoising via Meta-Learning [PDF]

, 2020
Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim   +3 more
openalex   +1 more source

Self-Supervised and Supervised Deep Learning for PET Image Reconstruction [PDF]

, 2023
Andrew J. Reader
openalex   +1 more source

Self-supervised Metric Learning

, 2022
H Μάθηση Μετρικής είναι ένα σημαντικό παράδειγμα για μία πληθώρα προβλημάτων της Μηχανικής Μάθησης και της Όρασης Υπολογιστών. Έχει επιτυχημένα εφαρμοστεί σε ε- φαρμογές όπως η λεπτομερής ταξινόμηση, ανάκτηση πληροφορίας, αναγνώριση προσώ- που κ.α. Αφορά την εκμάθηση μιας μετρικής απόστασης που βασίζεται στον προσδιορι- σμό ομοιοτήτων ή ανομοιοτήτων ...
openaire   +1 more source