1 code implementation • 22 Apr 2024 • David Campos, Bin Yang, Tung Kieu, Miao Zhang, Chenjuan Guo, Christian S. Jensen
The first difficulty in enabling continual calibration on the edge is that the full training data may be too large and thus not always available on edge devices.
1 code implementation • 24 Feb 2023 • David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, Christian S. Jensen
First, we propose adaptive ensemble distillation that assigns adaptive weights to different base models such that their varying classification capabilities contribute purposefully to the training of the lightweight model.
no code implementations • 22 Nov 2021 • David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, Christian S. Jensen
To improve accuracy, the ensemble employs multiple basic outlier detection models built on convolutional sequence-to-sequence autoencoders that can capture temporal dependencies in time series.