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Found 9 papers, 5 papers with code
Unveiling the Potential of Superexpressive Networks in Implicit Neural Representations
In this study, we examine the potential of one of the ``superexpressive'' networks in the context of learning neural functions for representing complex signals and performing machine learning downstream tasks.
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Complex physical systems are often described by partial differential equations (PDEs) that depend on parameters such as the Reynolds number in fluid mechanics.
Hawkes Process Based on Controlled Differential Equations
However, existing neural network-based Hawkes process models not only i) fail to capture such complicated irregular dynamics, but also ii) resort to heuristics to calculate the log-likelihood of events since they are mostly based on neural networks designed for regular discrete inputs.
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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.
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e. g., every month, iii) trains our time-series forecasting model with the extracted time- series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time- series data, i. e., neural controlled differential equations (NCDEs).
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.
LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising
Among various such mobile billboards, taxicab rooftop devices are emerging in the market as a brand new media.
ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks.
