TY - CONF
T1 - Dynamic Sparse Network for Time Series Classification: Learning What to “See”
AU - Xiao, Qiao
AU - Zhang, Yu
AU - Liu, Shiwei
AU - Pechenizkiy, Mykola
AU - Mocanu, Elena
AU - Mocanu, Decebal Constantin
PY - 2023/5/1
Y1 - 2023/5/1
N2 - The receptive field (RF), which determines the region of time series to be “seen” and used, is critical to improve the performance for time series classification (TSC). However, the variation of signal scales across and within time series data, makes it challenging to decide on proper RF sizes for TSC. In this paper, we propose a dynamic sparse network (DSN) with sparse connections for TSC, which can learn to cover various RF without cumbersome hyper-parameters tuning. The kernels in each sparse layer are sparse and can be explored under the constraint regions by dynamic sparse training, which makes it possible to reduce the resource cost. The experimental results show that the proposed DSN model can achieve state-of-art performance on both univariate and multivariate TSC datasets with less than 50% computational cost compared with recent baseline methods, opening the path towards more accurate resource-aware methods for time series analyses. Our code is provided in the supplementary material, and it will be made available online.
AB - The receptive field (RF), which determines the region of time series to be “seen” and used, is critical to improve the performance for time series classification (TSC). However, the variation of signal scales across and within time series data, makes it challenging to decide on proper RF sizes for TSC. In this paper, we propose a dynamic sparse network (DSN) with sparse connections for TSC, which can learn to cover various RF without cumbersome hyper-parameters tuning. The kernels in each sparse layer are sparse and can be explored under the constraint regions by dynamic sparse training, which makes it possible to reduce the resource cost. The experimental results show that the proposed DSN model can achieve state-of-art performance on both univariate and multivariate TSC datasets with less than 50% computational cost compared with recent baseline methods, opening the path towards more accurate resource-aware methods for time series analyses. Our code is provided in the supplementary material, and it will be made available online.
M3 - Poster
T2 - ICLR 2023 Workshop on Sparsity in Neural Networks
Y2 - 5 May 2023 through 5 May 2023
ER -