Hawkes Process Based on Controlled Differential Equations

1 code implementation • 9 May 2023 • Minju Jo, Seungji Kook, Noseong Park

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.

Irregular Time Series Time Series

Learnable Path in Neural Controlled Differential Equations

no code implementations • 11 Jan 2023 • Sheo Yon Jhin, Minju Jo, Seungji Kook, Noseong Park, Sungpil Woo, Sunhwan Lim

Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing.

Irregular Time Series Time Series +2

TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

no code implementations • 8 Nov 2022 • Seoyoung Hong, Minju Jo, Seungji Kook, Jaeeun Jung, Hyowon Wi, Noseong Park, Sung-Bae Cho

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).

Collaborative Filtering Recommendation Systems +2

LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations

1 code implementation • ICLR 2022 • Jaehoon Lee, Jinsung Jeon, Sheo Yon Jhin, Jihyeon Hyeong, Jayoung Kim, Minju Jo, Kook Seungji, Noseong Park

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.

Time Series Time Series Analysis

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

no code implementations • 19 Apr 2022 • Sheo Yon Jhin, Jaehoon Lee, Minju Jo, Seungji Kook, Jinsung Jeon, Jihyeon Hyeong, Jayoung Kim, Noseong Park

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.

BIG-bench Machine Learning Irregular Time Series +3

LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising

1 code implementation • 11 Aug 2021 • Jinsung Jeon, Soyoung Kang, Minju Jo, Seunghyeon Cho, Noseong Park, Seonghoon Kim, Chiyoung Song

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

1 code implementation • 31 May 2021 • Sheo Yon Jhin, Minju Jo, Taeyong Kong, Jinsung Jeon, Noseong Park

Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks.