Transfer learning on UR robots by Yu, Xiwei

“…As neural networks and deep learning develop, researchers are continually exploring the capabilities and potential of using data-driven methods to control robots. …”
Get full text

Descriptor learning for efficient retrieval by Philbin, J, Isard, M, Sivic, J, Zisserman, A

“…Scalable, stochastic gradient methods are used for the optimization.</p> <br> <p>For the case of particular object retrieval, we demonstrate impressive gains in performance on a ground truth dataset: our learnt 32-D descriptor without spatial re-ranking outperforms a baseline method using 128-D SIFT descriptors with spatial re-ranking.…”

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. …”
Get full text

Vision language representation learning by Yang, Xiaofeng

“…To address this, we propose an inductive logic programming method that enables the explanation of vision language models in formal logic language.…”
Get full text

Robust partial-to-partial point cloud registration in a full range by Pan, Liang, Cai, Zhongang, Liu, Ziwei

“…Extensive experiments show that GMCNet outperforms previous state-of-the-art methods for PPR.…”
Get full text

Image retrieval with deep learning by Tan, Joe Chin Yong

“…The query images are distorted with the 3 methods mentioned with different values of sigma, variance and quality. …”
Get full text

Efficient rare event sampling with unsupervised normalizing flows by Asghar, Solomon, Pei, Qing-Xiang, Volpe, Giorgio, Ni, Ran

“…Classical computational methods to sample rare events remain prohibitively inefficient and are bottlenecks for enhanced samplers that require prior data. …”
Get full text

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. …”
Get full text

Online learning for search and classification by Nguyen, Thanh Tam

“…(i) Feature selection: we have investigated a number of newly supervised term weighting methods to improve the performance of text classification; (ii) Online classification: we have proposed several online learning algorithms that can be used for topic classification; (iii) Two-view online learning: we have proposed a two-view online learning algorithm, which can work on two-view datasets; (iv) Online learning-to-rank: for search engine, we have proposed an online learning-to-rank algorithm, which was to learn a scoring function to re-rank the search result.…”
Get full text

SP.691 / WGS.691 Studies in Women's Life Narratives: Feminist Inquiry, Spring 2009 by Maher, Frinde, Bergland, Renee

Get full text

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. …”
Get full text

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. …”

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. …”
Get full text

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.…”
Get full text

21A.112 Seminar in Ethnography and Fieldwork, Fall 2003 by Silbey, Susan S.

Get full text

Unsupervised generative variational continual learning by Liu, Guimeng

“…Recent research in continual learning generally incorporates two of these methods to obtain better performance. …”
Get full text

Descriptor learning using convex optimisation by Simonyan, K, Vedaldi, A, Zisserman, A

“…It is demonstrated that the new learning methods improve over the state of the art in descriptor learning for large scale matching, Brown et al. [2], and large scale object retrieval, Philbin et al. [10].…”

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.…”
Get full text

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. …”
Get full text
Get full text

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. …”
Get full text