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

“…Due to the limited availability of expert-labeled anomalous samples in medical imaging applications such as chest X-rays, most existing unsupervised anomaly detection methods rely on only normal imaging data for training. …”
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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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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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16.90 Computational Methods in Aerospace Engineering, Spring 2014 by Willcox, Karen, Wang, Qiqi

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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

“…The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based Internet of Things. …”
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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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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. …”

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. …”

Incremental learning technologies for semantic segmentation by Yang, Yizhuo

“…To solve this problem, an incremental learning method: Combination of Old Prediction and Modified Label (COPML) is developed in this dissertation project. …”
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Modeling e-learning effectiveness and usability by Leong, Lock Wah.

“…The Kirkpatrick model and the Usability method are combined into questionnaire to obtain feedback from users after their E-Learning training. …”
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Refining learning models in grammatical inference by Wang, Xiangrui

“…We introduce the use of recurrent neural networks (RNNs) and present a pruning learning method to avoid the exponential space costs. …”
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Image classification by multimodal subspace learning by Yu, Jun, Lin, Feng, Seah, Hock Soon, Li, Cuihua, Lin, Ziyu

“…In recent years we witnessed a surge of interest in subspace learning for image classification. However, the previous methods lack of high accuracy since they do not consider multiple features of the images. …”
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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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Deep learning for fabric defect detection by Nangia, Saniya

“…The performance of models such as Cascade R-CNN, Faster R-CNN and RetinaNet is also analysed in comparison to the base models and modified StackBox method.…”
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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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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. …”

Imbalanced data learning for biomedical application by Xie, Vincent JianHan.

“…This report explores a method of imbalance data learning, investigating the use of HRV parameters and vital signs as predictor of cardiac arrest occurring 72 hours within hospital admission. …”
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