Results 41 to 50 of about 414,528 (301)
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
, 2019 The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems.
Ahn, Euijoon +4 more
core +1 more source
Advanced Functional Materials, EarlyView.Molten KNO2 treatment induces a lowrefractive index disordered carbon shell on fluorescent nanodiamonds, enhancing fluorescence emission while preserving spin coherence. Machine learningassisted correlative TEMPL enables direct single‐particle resolution of this enhancement relative to air‐oxidized nanodiamonds of similar morphology, establishing a ...
Parkarsh Kumar +10 more
wiley +1 more source
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
An Unsupervised Learning Approach to Condition Assessment on a Wound-Rotor Induction Generator
Energies, 2021 Accurate online diagnosis of incipient faults and condition assessment on generators is especially challenging to automate through supervised learning techniques, because of data imbalance.
Elsie Swana, Wesley Doorsamy
doaj +1 more source
Advanced Materials, EarlyView.Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam +6 more
wiley +1 more source
Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
Energies, 2021 It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems.
Sungil Kim +3 more
doaj +1 more source
Accuracy of Latent-Variable Estimation in Bayesian Semi-Supervised Learning [PDF]
, 2015 Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process ...
Yamazaki, Keisuke
core
Unsupervised Learning of Semantic Audio Representations
, 2017 Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds.
Ellis, Daniel P. W. +7 more
core +1 more source
Advanced Materials, EarlyView.The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
A Deep Embedded Clustering Algorithm for the Binning of Metagenomic Sequences
IEEE Access, 2022 The study of metagenomic sequences brings a deep understanding of microbial communities. One of the crucial steps in metagenomic projects is to classify sequences into different organisms, named the binning problem.
Huynh Quang Bao +2 more
doaj +1 more source