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Found 4 papers, 1 papers with code
FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics
In this paper, we revisit the Taylor series expansion from a modern Machine Learning perspective.
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties
Probabilistic forecasting of complex phenomena is paramount to various scientific disciplines and applications.
Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings
In this paper, we show efficient strategies that circumvent this problem by differentiating through the operations of a power flow solver that embeds the power flow equations into a holomorphic function.
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
We propose spectral methods for long-term forecasting of temporal signals stemming from linear and nonlinear quasi-periodic dynamical systems.
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