Automated Sub-Zoning of Water Distribution Systems by Allen, Michael, Preis, Ami, Iqbal, Mudasser, Perelman, Lina Sela, Whittle, Andrew

“…This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Global clustering – a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure – a bottom-up algorithm based on the property of network modularity, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning – a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. …”
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Ambient Sound Provides Supervision for Visual Learning by Owens, Andrew Hale, Wu, Jiajun, McDermott, Joshua H., Freeman, William T., Torralba, Antonio

“…We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.…”
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Multi-level automated sub-zoning of water distribution systems by Allen, Micheal, Preis, Ami, Perelman, Lina Sela, Whittle, Andrew

“…This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Graph clustering – a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure – a bottom-up algorithm based on network modularity property, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning – a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. …”
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Skip-thought Vectors by Kiros, Ryan, Zhu, Yukun, Salakhutdinov, Ruslan R., Zemel, Richard, Urtasun, Raquel, Fidler, Sanja, Torralba, Antonio

“…We describe an approach for unsupervised learning of a generic, distributed sentence encoder. …”
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Reducibility and computational lower bounds for problems with planted sparse structure by Brennan, Matthew (Matthew Stewart)

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The societal and environmental impacts of the skincare industry : a case for innovation by Maupin, Meghan

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Detection of RNA structural heterogeneity : computational analysis and development of web-based tool by Persad, Sitara C

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Deep learning methods for the design and understanding of solid materials by Xie, Tian,Ph.D.Massachusetts Institute of Technology.

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Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment by Rangaprakash, D., Odemuyiwa, Toluwanimi, Narayana Dutt, D., Deshpande, Gopikrishna

“…Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. …”
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Learning poisson binomial distributions by Daskalakis, Constantinos, Diakonikolas, Ilias, Servedio, Rocco A.

“…We consider a basic problem in unsupervised learning: learning an unknown \emph{Poisson Binomial Distribution}. …”
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Building 3D Generative Models from Minimal Data by Sutherland, Skylar, Egger, Bernhard, Tenenbaum, Joshua

“…We extend our model to a preliminary unsupervised learning framework that enables the learning of the distribution of 3D faces using one 3D template and a small number of 2D images. …”
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Learning from partially labeled data by Szummer, Marcin Olof

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Assisted auscultation : creation and visualization of high dimensional feature spaces for the detection of mitral regurgitation by Leeds, Daniel Demeny

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Modelling patient states in intensive care patients by Kshetri, Kanak Bikram

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Integrative annotation of chromatin elements from ENCODE data by Ernst, Jason, Kellis, Manolis, Hoffman, Michael M., Wilder, Steven P., Kundaje, Anshul, Harris, Robert S., Libbrecht, Max, Giardine, Belinda, Ellenbogen, Paul M., Bilmes, Jeffrey A., Birney, Ewan, Hardison, Ross C., Dunham, Ian, Noble, William Stafford

“…To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. …”
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High dimensional clustering for mixture models by Liu, Yiming

“…Clustering is an essential subject in unsupervised learning. It is a common technique used in many fields, including machine learning, statistics, bioinformatics, and computer graphics. …”
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Domain adversarial training for speech enhancement by Hou, Nana, Xu, Chenglin, Chng, Eng Siong, Li, Haizhou

“…As the proposed approach is able to adapt to a new domain only with noisy speech data in target domain, we call it an unsupervised learning technique. Experiments suggest that our approach delivers voice quality comparable with other supervised learning techniques that require clean-noisy parallel data.…”
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Dynamically growing neural network architecture for lifelong deep learning on the edge by Piyasena, Duvindu, Thathsara, Miyuru, Kanagarajah, Sathursan, Lam,Siew-Kei, Wu, Meiqing

“…The proposed SONN architecture is capable of performing unsupervised learning on input features from the CNN by dynamically growing neurons and connections. …”
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Water leak detection with data analytics by Cheong, Yun Cai

“…Data cleaning was then conducted before executing the machine learning portion of this project. Through unsupervised learning, the K-means algorithm utilized in this project clusters the data set and is thus able to detect water leakage based on certain assumptions. …”
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On the design of capacity-approaching error-correction codes for multi constrained systems by Zhang, Jiayu

“…In general, the specific similarity is not known, so sequence clusters generated by these greedy algorithms tend not to match the actual clusters if an imperfect parameter is used. As an unsupervised learning model, mean shift algorithm has been utilised many times in several fields like descriptive statistics, audio processing, and computer vision. …”
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