Quantifying Behavior Using Deep Learning

被引:0
|
作者
Maree, Liezl [1 ]
Leonardis, Eric [1 ]
Gepshtein, Sergei [1 ]
Albright, Thomas [1 ]
Hitchcock, Kristianna [1 ]
Andrews, Nick [1 ]
Azim, Eiman [1 ]
Pfaff, Samuel [1 ]
Metallo, Christian [1 ]
Pereira, Talmo [1 ]
机构
[1] Salk Inst Biol Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037 USA
关键词
Deep Learning Technology; Deep Phenotyping; Biological Motion;
D O I
暂无
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
引用
收藏
页码:S7 / S7
页数:1
相关论文
共 50 条
  • [21] Antisocial online behavior detection using deep learning
    Zinovyeva, Elizaveta
    Hardle, Wolfgang Karl
    Lessmann, Stefan
    DECISION SUPPORT SYSTEMS, 2020, 138
  • [22] Character Behavior Automation Using Deep Reinforcement Learning
    Lee, Hyunki
    Dahouda, Mwamba Kasongo
    Joe, Inwhee
    IEEE ACCESS, 2023, 11 : 101435 - 101442
  • [23] Webthetics: Quantifying webpage aesthetics with deep learning
    Dou, Qi
    Zheng, Xianjun Sam
    Sun, Tongfang
    Heng, Pheng-Ann
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 2019, 124 : 56 - 66
  • [24] Quantifying Uncertainty in Deep Learning of Radiologic Images
    Faghani, Shahriar
    Moassefi, Mana
    Rouzrokh, Pouria
    Khosravi, Bardia
    Baffour, Francis I.
    Ringler, Michael D.
    Erickson, Bradley J.
    RADIOLOGY, 2023, 308 (02)
  • [25] Quantifying the Alignment of Graph and Features in Deep Learning
    Qian, Yifan
    Expert, Paul
    Rieu, Tom
    Panzarasa, Pietro
    Barahona, Mauricio
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (04) : 1663 - 1672
  • [26] QIM: Quantifying Hyperparameter Importance for Deep Learning
    Jia, Dan
    Wang, Rui
    Xu, Chengzhong
    Yu, Zhibin
    NETWORK AND PARALLEL COMPUTING, 2016, 9966 : 180 - 188
  • [27] Quantifying the Impact of Memory Errors in Deep Learning
    Zhang, Zhao
    Huang, Lei
    Huang, Ruizhu
    Xu, Weijia
    Katz, Daniel S.
    2019 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER), 2019, : 125 - 136
  • [28] DigitalExposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning
    Thomas Johnson
    Eiman Kanjo
    Kieran Woodward
    Computational Urban Science, 3
  • [29] Quantifying Soybean Defects: A Computational Approach to Seed Classification Using Deep Learning Techniques
    Sable, Amar
    Singh, Parminder
    Kaur, Avinash
    Driss, Maha
    Boulila, Wadii
    AGRONOMY-BASEL, 2024, 14 (06):
  • [30] DigitalExposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning
    Johnson, Thomas
    Kanjo, Eiman
    Woodward, Kieran
    COMPUTATIONAL URBAN SCIENCE, 2023, 3 (01):