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Found 4 papers, 0 papers with code
Interpretable Super-Resolution via a Learned Time-Series Representation
We develop an interpretable and learnable Wigner-Ville distribution that produces a super-resolved quadratic signal representation for time-series analysis.
Spline Filters For End-to-End Deep Learning
We propose to tackle the problem of end-to-end learning for raw waveform signals by introducing learnable continuous time-frequency atoms.
Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion
Deep Neural Networks (DNNs) provide state-of-the-art solutions in several difficult machine perceptual tasks.
Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants
In this paper we propose a scalable version of a state-of-the-art deterministic time-invariant feature extraction approach based on consecutive changes of basis and nonlinearities, namely, the scattering network.
