
Positively Weighted Kernel Quadrature via Subsampling
We study kernel quadrature rules with positive weights for probability m...
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Neural Controlled Differential Equations for Online Prediction Tasks
Neural controlled differential equations (Neural CDEs) are a continuous...
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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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SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Making predictions and quantifying their uncertainty when the input data...
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Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder
Bipolar disorder (BD) and borderline personality disorder (BPD) are two ...
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SKTree: a systematic malware detection algorithm on streaming trees via the signature kernel
The development of machine learning algorithms in the cyber security dom...
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Neural SDEs as InfiniteDimensional GANs
Stochastic differential equations (SDEs) are a staple of mathematical mo...
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Estimating the probability that a given vector is in the convex hull of a random sample
For a ddimensional random vector X, let p_n, X be the probability that ...
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The shifted ODE method for underdamped Langevin MCMC
In this paper, we consider the underdamped Langevin diffusion (ULD) and ...
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Information Extraction from Swedish Medical Prescriptions with SigTransformer Encoder
Relying on large pretrained language models such as Bidirectional Encode...
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"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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Neural CDEs for Long Time Series via the LogODE Method
Neural Controlled Differential Equations (Neural CDEs) are the continuou...
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Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in NonClinical Interviews
Bipolar disorder (BD) and borderline personality disorder (BPD) are both...
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Computing the full signature kernel as the solution of a Goursat problem
Recently there has been an increased interested in the development of ke...
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Anomaly detection on streamed data
We introduce powerful but simple methodology for identifying anomalous o...
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A Generalised Signature Method for Time Series
The `signature method' refers to a collection of feature extraction tech...
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Generalised Interpretable Shapelets for Irregular Time Series
The shapelet transform is a form of feature extraction for time series, ...
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Neural Controlled Differential Equations for Irregular Time Series
Neural ordinary differential equations are an attractive option for mode...
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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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Learning stochastic differential equations using RNN with log signature features
This paper contributes to the challenge of learning a function on stream...
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Insertion algorithm for inverting the signature of a path
In this article we introduce the insertion method for reconstructing the...
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Universal Approximation with Deep Narrow Networks
The classical Universal Approximation Theorem certifies that the univers...
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Deep Signatures
The signature is an infinite graded sequence of statistics known to char...
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Random forest prediction of Alzheimer's disease using pairwise selection from time series data
Timedependent data collected in studies of Alzheimer's disease usually ...
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Labelling as an unsupervised learning problem
Unravelling hidden patterns in datasets is a classical problem with many...
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Sketching the order of events
We introduce features for massive data streams. These stream features ca...
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Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signaturebased model
Recurrent major mood episodes and subsyndromal mood instability cause su...
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A signaturebased machine learning model for bipolar disorder and borderline personality disorder
Mobile technologies offer opportunities for higher resolution monitoring...
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Leveraging the Path Signature for Skeletonbased Human Action Recognition
Human action recognition in videos is one of the most challenging tasks ...
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Learning SpatialSemantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Online handwritten Chinese text recognition (OHCTR) is a challenging pro...
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