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Found 6 papers, 4 papers with code
Precursor-of-Anomaly Detection for Irregular Time Series
Unlike conventional anomaly detection, which focuses on determining whether a given time series observation is an anomaly or not, PoA detection aims to detect future anomalies before they happen.
Learnable Path in Neural Controlled Differential Equations
Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing.
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
The problem of processing very long time-series data (e. g., a length of more than 10, 000) is a long-standing research problem in machine learning.
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks.
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting
Neural networks inspired by differential equations have proliferated for the past several years.
ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks.
