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An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications
Furthermore, at inference time, ENAMLE adaptively alters the number of the ensemble of models based on the amount of missing data rate and the energy-accuracy trade-off.
F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning
Experiment evaluation on five action recognition benchmark datasets, i. e., Something-Something-v1, SomethingSomething-v2, Kinetics-400, UCF101, and HMDB51 demonstrate the effectiveness of the proposed F4D network architecture for video-level action recognition.
Towards Modular Machine Learning Solution Development: Benefits and Trade-offs
In this work we explore the benefits of modular machine learning solutions and discuss how modular machine learning solutions can overcome some of the major solution engineering limitations of monolithic machine learning solutions.
SECOE: Alleviating Sensors Failure in Machine Learning-Coupled IoT Systems
Unfortunately, the performance of many ML applications quickly degrades when faced with data incompleteness.
