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02/04/2022
On Neural Differential Equations
The conjoining of dynamical systems and deep learning has become a topic...
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10/30/2021
Equinox: neural networks in JAX via callable PyTrees and filtered transformations
JAX and PyTorch are two popular Python autodifferentiation frameworks. J...
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06/21/2021
Neural Controlled Differential Equations for Online Prediction Tasks
Neural controlled differential equations (Neural CDEs) are a continuous-...
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05/27/2021
Efficient and Accurate Gradients for Neural SDEs
Neural SDEs combine many of the best qualities of both RNNs and SDEs, an...
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02/06/2021
Neural SDEs as Infinite-Dimensional GANs
Stochastic differential equations (SDEs) are a staple of mathematical mo...
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09/20/2020
"Hey, that's not an ODE": Faster ODE Adjoints with 12 Lines of Code
Neural differential equations may be trained by backpropagating gradient...
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09/17/2020
Neural CDEs for Long Time Series via the Log-ODE Method
Neural Controlled Differential Equations (Neural CDEs) are the continuou...
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06/01/2020
A Generalised Signature Method for Time Series
The `signature method' refers to a collection of feature extraction tech...
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05/28/2020
Generalised Interpretable Shapelets for Irregular Time Series
The shapelet transform is a form of feature extraction for time series, ...
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05/18/2020
Neural Controlled Differential Equations for Irregular Time Series
Neural ordinary differential equations are an attractive option for mode...
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01/03/2020
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
Signatory is a library for calculating signature and logsignature transf...
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05/21/2019
Universal Approximation with Deep Narrow Networks
The classical Universal Approximation Theorem certifies that the univers...
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05/21/2019