Unsupervised Learning of Morphology by Harald Hammarström, Lars Borin

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Unsupervised Learning of Morphological Forests by Jiaming Luo, Karthik Narasimhan, Regina Barzilay

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Unsupervised learning analysis on the proteomes of Zika virus by Edgar E. Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo

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An Unsupervised Learning Method for Radio Interferometry Deconvolution by Lei Yu, Bin Liu, Cheng-Jin Jin, Ru-Rong Chen, Hong-Wei Xi, Bo Peng

“…The resulting method is a novel, fully interpretable unsupervised learning approach that combines the mathematical rigor of CS with the expressive power of deep neural networks, effectively bridging the gap between deep learning and classical dictionary methods. …”
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Tumor detection on bronchoscopic images by unsupervised learning by Qingqing Liu, Haoliang Zheng, Zhiwei Jia, Zhihui Shi

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Unsupervised Learning of the Morphology of a Natural Language by John Goldsmith

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The Convallis rule for unsupervised learning in cortical networks. by Pierre Yger, Kenneth D Harris

“…Here we describe a framework for cortical synaptic plasticity termed the "Convallis rule", mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. …”
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Unsupervised learning of temporal regularities in visual cortical populations by Sorin Pojoga, Ariana Andrei, Valentin Dragoi

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Detecting anomalous SRF cavity behavior with unsupervised learning by Hal Ferguson, Jiang Li, Adam Carpenter, Chris Tennant, Dillon Thomas, Dennis Turner

“…We present an unsupervised learning framework for detecting anomalous superconducting radio-frequency (SRF) cavity behavior at the Continuous Electron Beam Accelerator Facility (CEBAF), emphasizing its initial performance and effectiveness. …”
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An Energy-Domain IR NUC Method Based on Unsupervised Learning by Ting Li, Xuefeng Lai, Sheng Liao, Yucheng Xia

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Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning by Ilya Larin, Alexander Karabelsky

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Unsupervised Learning for Heart Disease Prediction: Clustering-Based Approach by Jetty Janani., Sk Sajida Sultana., Polepalle Ranga Bhavitha., Parusu Vishwitha.

“…It, however, opens up the possibility of unsupervised learning in health analytics and shows how this can be applied to the role of machine learning for early detection and targeted treatment, thereby contributing to better patient outcomes and proactivity in managing heart disease risks.…”
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Four-path unsupervised learning-based image defogging network by Wei LIU, Cheng CHEN, Rui JIANG, Tao LU

Subjects: “…cycle generative adversarial network;single image defogging;atmospheric scattering model;attention feature fusion;unsupervised learning…”
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Low‐Frequency Reconstruction for Full Waveform Inversion by Unsupervised Learning by Ningcheng Cui, Tao Lei, Wei Zhang

“…In this study, we employed an unsupervised learning method, namely cycle‐consistent adversarial networks (CycleGAN), to reconstruct large‐scale‐feature related low‐frequency information based on the high‐frequency input data. …”
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FEATURE SELECTION COMPARATIVE PERFORMANCE FOR UNSUPERVISED LEARNING ON CATEGORICAL DATASET by Rachmad Fitriyanto, Mohamad Ardi

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Identification of Writing Strategies in Educational Assessments with an Unsupervised Learning Measurement Framework by Cheng Tang, Jiawei Xiong, George Engelhard

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Detection of Attacks in Network Traffic with the Autoencoder-Based Unsupervised Learning Method by Yalçın Özkan

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Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning by J. Enrique Sierra-García, Matilde Santos

“…The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the integral of the error feeds the learning algorithm. …”
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Research on Image Super-Resolution Reconstruction Technology Based on Unsupervised Learning by Shuo Han, Bo Mo, Jie Zhao, Bolin Pan, Yiqi Wang

“…Firstly, a natural image degradation model based on a generative adversarial network is designed to learn the degradation relationship between image blocks within the image; then, an unsupervised learning residual network is designed based on the idea of image self-similarity to complete image super-resolution reconstruction. …”
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Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions by Grazia Iadarola, Alessandro Mingotti, Virginia Negri, Susanna Spinsante

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