Items where Author is "Taylor, Ben"

Journal Article

Smith, Charlotte Caroline and Healey, John R. and De Berenguer Cesar, Erika and Young, Paul and Taylor, Ben and Elias, Fernando and Espirito-Santo, Fernando and Barlow, Jos (2021) Old-growth forest loss and secondary forest recovery across Amazonian countries. Environmental Research Letters, 16 (8): 085009. ISSN 1748-9326

Sanz Marco, Vicent and Taylor, Ben and Wang, Zheng and Elkhatib, Yehia (2020) Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection. ACM Transactions on Embedded Computing, 19 (1): 2. ISSN 1539-9087

Contribution in Book/Report/Proceedings

Taylor, Ben and Sanz Marco, Vicent and Wolff, Willy and Elkhatib, Yehia and Wang, Zheng (2018) Adaptive Deep Learning Model Selection on Embedded Systems. In: LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems :. ACM, New York, pp. 31-43. ISBN 9781450358033

Sanz Marco, Vicent and Taylor, Ben and Porter, Barry Francis and Wang, Zheng (2017) Improving Spark Application Throughput Via Memory Aware Task Co-location : A Mixture of Experts Approach. In: Middleware '17 Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference :. ACM, New York, pp. 95-108. ISBN 9781450347204

Taylor, Ben and Sanz Marco, Vicent and Wang, Zheng (2017) Adaptive optimization for OpenCL programs on embedded heterogeneous systems. In: LCTES 2017 Proceedings of the 18th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems :. ACM, New York, pp. 11-20. ISBN 9781450350303

Ye, Guixin and Tang, Zhanyong and Fang, Dingyi and Chen, Xiaojiang and Kim, Kwang In and Taylor, Ben and Wang, Zheng (2017) Cracking Android pattern lock in five attempts. In: Proceedings 2017 Network and Distributed System Security Symposium 2017 (NDSS'17) :. Internet Society, Reston VA, 0-0. ISBN 1891562460

Thesis

Taylor, Ben and Wang, Zheng and Porter, Barry (2020) Efficient deep neural network inference for embedded systems : A mixture of experts approach. PhD thesis, Lancaster University.