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Non-adversarial training of Neural SDEs with signature kernel scores
Neural SDEs are continuous-time generative models for sequential data. State-of-the-art
performance for irregular time series generation has been previously obtained by training …
performance for irregular time series generation has been previously obtained by training …
Neural signature kernels as infinite-width-depth-limits of controlled resnets
Motivated by the paradigm of reservoir computing, we consider randomly initialized
controlled ResNets defined as Euler-discretizations of neural controlled differential …
controlled ResNets defined as Euler-discretizations of neural controlled differential …
Efficient and accurate gradients for neural sdes
Neural SDEs combine many of the best qualities of both RNNs and SDEs, and as such are a
natural choice for modelling many types of temporal dynamics. They offer memory efficiency …
natural choice for modelling many types of temporal dynamics. They offer memory efficiency …
Koopman kernel regression
Many machine learning approaches for decision making, such as reinforcement learning,
rely on simulators or predictive models to forecast the time-evolution of quantities of interest …
rely on simulators or predictive models to forecast the time-evolution of quantities of interest …
Lecture notes on rough paths and applications to machine learning
T Cass, C Salvi - ar** via distribution regression: a higher rank signature approach
B Horvath, M Lemercier, C Liu, T Lyons… - ar** the
law of a stochastic process to a scalar target. The learning procedure based on the notion of …
law of a stochastic process to a scalar target. The learning procedure based on the notion of …
Random fourier signature features
Tensor algebras give rise to one of the most powerful measures of similarity for sequences
of arbitrary length called the signature kernel accompanied with attractive theoretical …
of arbitrary length called the signature kernel accompanied with attractive theoretical …