TH4.R12.4
A DOCKER-BASED FRAMEWORK FOR PARALLEL HYPERPARAMETER TUNING OF LSTM MODELS
Anusha Srirenganathan Malarvizhi, George Mason University; Tayven Stover, Northern Virginia Community College; Seren Smith, George Mason University; Kaylee Smith, University of Michigan; Zifu Wang, Harvard University; Chaowei Yang, George Mason University
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, 17:00 - 17:15
Session Chair:
Gabriele Cavallaro, University of Iceland
Presentation
Discussion
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
TH4.R12.2: ENERGY EFFICIENT GPU FREQUENCY SCALING FOR GEOSPATIAL FOUNDATION MODELS
Joseph Arnold Xavier, Rocco Sedona, Forschungszentrum Jülich; Morris Riedel, Gabriele Cavallaro, University of 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; Kaidi Chen, STAR.VISION Aerospace Group Limited; Chaojie Zhang, Zhejiang University
TH4.R12.4: A DOCKER-BASED FRAMEWORK FOR PARALLEL HYPERPARAMETER TUNING OF LSTM MODELS
Anusha Srirenganathan Malarvizhi, George Mason University; Tayven Stover, Northern Virginia Community College; Seren Smith, George Mason University; Kaylee Smith, University of Michigan; Zifu Wang, Harvard University; Chaowei Yang, George Mason University
Resources
No resources available.