2020
DOI: 10.48550/arxiv.2008.02397
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DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data

Mohammad Malekzadeh,
Richard G. Clegg,
Andrea Cavallaro
et al.

Abstract: Current deep neural architectures for processing sensor data are mainly designed for data coming from a fixed set of sensors, with a fixed sampling rate. Changing the dimensions of the input data causes considerable accuracy loss, unnecessary computations, or application failures. To address this problem, we introduce a dimension-adaptive pooling (DAP) layer that makes deep architectures robust to temporal changes in sampling rate and in sensor availability. DAP operates on convolutional filter maps of variabl… Show more

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