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pro vyhledávání: '"Jacobs, Michael"'
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environments evolve rapidly and can be generated by edge devices, the algorithm
Externí odkaz:
http://arxiv.org/abs/2306.05310
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging. However, the environments for medical imaging tasks are constantly evolving in terms of imaging orientations, imaging sequences, and pathologies. To that end, we deve
Externí odkaz:
http://arxiv.org/abs/2306.00188
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. However, this framework still requires a centralized glob
Externí odkaz:
http://arxiv.org/abs/2303.06783
Selective experience replay is a popular strategy for integrating lifelong learning with deep reinforcement learning. Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting. Furthermore,
Externí odkaz:
http://arxiv.org/abs/2302.11510
Autor:
Madondo, Malvern, Azmat, Muneeza, Dipietro, Kelsey, Horesh, Raya, Jacobs, Michael, Bawa, Arun, Srinivasan, Raghavan, O'Donncha, Fearghal
Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, which exhibit distinct spatial and temporal variations. Managing resource inputs such as fertilizer and irrigation in th
Externí odkaz:
http://arxiv.org/abs/2302.04988