TH4.R12.2
ENERGY EFFICIENT GPU FREQUENCY SCALING FOR GEOSPATIAL FOUNDATION MODELS
Joseph Arnold Xavier, Rocco Sedona, Forschungszentrum Jülich, Germany; Morris Riedel, Gabriele Cavallaro, University of Iceland, Iceland
Session:
TH4.R12: High-Performance and Efficient Learning Frameworks for Remote Sensing Oral
Track:
AI and Big Data
Location:
Georgetown East
Presentation Time:
Thursday, 13 August, 16:30 - 16:45
Session Co-Chairs:
Gabriele Cavallaro, University of Iceland and Jorge Alberto Suárez Garea, Universidade de Santiago de Compostela
Presentation
Not logged in.
Not logged in.
Discussion
Not logged in.
Resources
No resources available.
Session TH4.R12
TH4.R12.1: SC-DBNET: AN EFFICIENT SPECTRAL-CONDITIONED DUAL-BRANCH NET FOR HSI CLASSIFICATION
Jihun Kim, Jungkwon Kim, Chi Zhang, JEONGHYEON PARK, Kwangsun Yoo, Seok-Joo Byun, ELROILAB, Korea (South)
TH4.R12.2: ENERGY EFFICIENT GPU FREQUENCY SCALING FOR GEOSPATIAL FOUNDATION MODELS
Joseph Arnold Xavier, Rocco Sedona, Forschungszentrum Jülich, Germany; Morris Riedel, Gabriele Cavallaro, University of Iceland, Iceland
TH4.R12.3: FROGNER: ANCHOR-DRIVEN NEURAL GAUSSIAN REPRESENTATION FOR COMPACT AND HIGH-FIDELITY NOVEL VIEW SYNTHESIS
Xinhao Deng, Hongwei Li, Guhong Zhang, Chengsai Zhou, Zhejiang University, China; Kaidi Chen, STAR.VISION Aerospace Group Limited, China; Chaojie Zhang, Zhejiang University, China
TH4.R12.4: A DOCKER-BASED FRAMEWORK FOR PARALLEL HYPERPARAMETER TUNING OF LSTM MODELS
Anusha Srirenganathan Malarvizhi, George Mason University, United States; Tayven Stover, Northern Virginia Community College, United States; Seren Smith, George Mason University, United States; Kaylee Smith, University of Michigan, United States; Zifu Wang, Harvard University, United States; Chaowei Yang, George Mason University, United States
Resources
No resources available.