2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-29.2
Paper Title CONTINUOUS CNN FOR NONUNIFORM TIME SERIES
Authors Hui Shi, University of California, San Diego, United States; Yang Zhang, MIT-IBM Watson AI Lab, United States; Hao Wu, University of Illinois at Urbana-Champaign, United States; Shiyu Chang, MIT-IBM Watson AI Lab, United States; Kaizhi Qian, Mark Hasegawa-Johnson, University of Illinois at Urbana-Champaign, United States; Jishen Zhao, University of California, San Diego, United States
SessionMLSP-29: Deep Learning for Time Series
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract CNN for time series data implicitly assumes that the data are uniformly sampled, whereas many event-based and multi-modal data are nonuniform or have heterogeneous sampling rates. Directly applying regular CNN to nonuniform time series is ungrounded, be-cause it is unable to recognize and extract common patterns from the nonuniform input signals. In this paper, we propose the Continuous CNN (CCNN), which estimates the inherent continuous inputs by interpolation, and performs continuous convolution on the continuous input. The interpolation and convolution kernels are learned in an end-to-end manner and are able to learn useful patterns despite the nonuniform sampling rate. Results of several experiments verify that CNN achieves a better performance on nonuniform data, and learns meaningful continuous kernels.