Optimization strategies for federated learning by Zhang, Tinghao

“…We achieve this through a deep reinforcement learning-based scheduling strategy and an optimized bandwidth allocation method, enabling FL to achieve target accuracy with reduced system costs. …”
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Reinforcement learning for robot assembly by Vuong Quoc Nghia

“…Finally, this thesis examines methods to narrow the reality gap - the fundamental problem in sim-to-real reinforcement learning. …”
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Vision language representation learning by Yang, Xiaofeng

“…Despite its significance, learning effective vision language representation remains challenging due to several reasons. …”
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iTD3-CLN: learn to navigate in dynamic scene through Deep Reinforcement Learning by Jiang, Haoge, Esfahani, Mahdi Abolfazli, Wu, Keyu, Wan, Kong-wah, Heng, Kuan-kian, Wang, Han, Jiang, Xudong

“…In contrast to the conventional methods such as the DWA, our approach is found superior in the following ways: no need for prior knowledge of the environment and metric map, lower reliance on an accurate sensor, learning emergent behavior in dynamic scene that is intuitive, and more remarkably, able to transfer to the real robot without further fine-tuning. …”
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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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