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A Dataset of Inertial Measurement Units for Handwritten English Alphabets
This paper presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style.
Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation
During the training of lightweight DNN, we introduce a novel early halting technique, which preserves network resources; thus, speedups the training procedure.
Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning
In this paper, we propose a federated learning approach to suppress the unequal distribution of the noisy labels in the dataset of each participant.
FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings
With the enhancement of people's living standards and rapid growth of communication technologies, residential environments are becoming smart and well-connected, increasing overall energy consumption substantially.
A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions
In this paper, we present a comprehensive review of existing literature on compressing DNN model that reduces both storage and computation requirements.
Approaches and Applications of Early Classification of Time Series: A Review
As most of the approaches have solved the early classification problem with different aspects, it becomes very important to make a thorough review of the existing solutions to know the current status of the area.
