MAS.714J / STS.445J Technologies for Creative Learning, Fall 2004 by Resnick, Mitchel

Subjects: “…learning…”
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16.901 Computational Methods in Aerospace Engineering, Spring 2003 by Darmofal, David L.

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15.053 Optimization Methods in Management Science, Spring 2007 by Mamani, Hamed, Orlin, James, Metzger, Michael, Narayanaswamy, Murali

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16.901 Computational Methods in Aerospace Engineering, Spring 2005 by Darmofal, David

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Explore anomaly detection and localization methods for medical imaging data by Yu, Tianze

“…Motivated by this, we introduce Contrastive Learning guided GAN (CLGAN), which leverages unlabeled mixed data and contrastive learning to guide the generation process. …”
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Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT / by Swarnalatha, P. (Purushotham), 1977-, editor 656271, Prabu, S., 1981-, editor 656272, IGI Global (Online service) 631983

“…"Deep Learning Techniques for Cloud-Based Industrial IoT aims to demonstrate how computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. …”
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Data-driven methods to predict the stability metrics of catalytic nanoparticles by Prabhu, Asmee M., Choksi, Tej S.

“…In this review we discuss the recent advances in data-driven methods to predict stability metrics of nanoparticles.…”
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ACDC: online unsupervised cross-domain adaptation by de Carvalho, Marcus, Pratama, Mahardhika, Zhang, Jie, Yee, Edward Yapp Kien

“…Our experimental results under the prequential test-then-train protocol indicate an improvement in target accuracy over the baseline methods, achieving more than a 10% increase in some cases.…”
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Anomaly detection in multivariate time series using ensemble method by Liu, Yanling

“…To improve the above-mentioned anomaly detection limitation of matrix profile, we propose and demonstrate two methods, the matrix profile with autoencoder method and the boosting method. …”
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Topics in Bayesian machine learning for finance by Spears, T

“…Further, we estimate an approximation to epistemic uncertainty via a pseudo-Bayesian deep learning method. This work demonstrates the utility of the model output for deciding the relative allocation of risk capital across trades. …”

16.90 Computational Methods in Aerospace Engineering, Spring 2014 by Willcox, Karen, Wang, Qiqi

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uCAP: an unsupervised prompting method for vision-language models by Nguyen, AT, Tai, KS, Chen, BC, Shukla, SN, Yu, H, Torr, P, Tian, TP, Lim, SN

“…To overcome this bottleneck, this paper proposes uCAP, a method to automatically learn domain-specific prompts/contexts using only unlabeled in-domain images. …”

Unsupervised generative variational continual learning by Liu, Guimeng

“…Recent research in continual learning generally incorporates two of these methods to obtain better performance. …”
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Domain transfer multiple kernel learning by Duan, Lixin, Tsang, Ivor Wai-Hung, Xu, Dong

“…Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. …”
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Descriptor learning using convex optimisation by Simonyan, K, Vedaldi, A, Zisserman, A

“…Both of these problems use large margin discriminative learning methods. The third contribution is a new method of obtaining the positive and negative training data in a weakly supervised manner. …”

Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning by Zhang, Chaoyu, Shi, Shanghao, Wang, Ning, Xu, Xiangxiang, Li, Shaoyu, Zheng, Lizhong, Marchany, Randy, Gardner, Mark, Hou, Y. Thomas, Lou, Wenjing

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Machine learning for microscopy image analysis by Azry, Amerul Tajuddin

“…This research project is based on microscopy image analysis and how machine learning can be used for work such as detection and classification to aid in the speed of processing multiple images where traditional methods fall off.…”
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Automating feature engineering in machine Learning by Zhu, Haoyu

“…Automated feature selection was also tested on wrapper methods by utilizing the Bayesian optimization method. …”
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Scientific machine learning for knowledge discovery by Siddesh Sambasivam Suseela

“…Finally, highlighting the key challenges in the current methods and future research directions in using machine learning to automate scientific knowledge discovery and incorporation.…”
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Deep learning with constrained data resource by Mao, Jiangtian

“…This report provides a solution to solve the few samples learning (FSL) problems. The method can achieve a better accuracy compared to simple full-supervised learning methods, especially the problem becomes to a one-shotting problem.…”
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