Time-based Sequence Model for Personalization and Recommendation Systems

1 code implementation • 27 Aug 2020 • Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich, Liang Xiong

In this paper we develop a novel recommendation model that explicitly incorporates time information.

Recommendation Systems

Machine Discovery of Partial Differential Equations from Spatiotemporal Data

1 code implementation • 15 Sep 2019 • Ye Yuan, Junlin Li, Liang Li, Frank Jiang, Xiuchuan Tang, Fumin Zhang, Sheng Liu, Jorge Goncalves, Henning U. Voss, Xiuting Li, Jürgen Kurths, Han Ding

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data.

Using Neural Networks to Compute Approximate and Guaranteed Feasible Hamilton-Jacobi-Bellman PDE Solutions

no code implementations • 10 Nov 2016 • Frank Jiang, Glen Chou, Mo Chen, Claire J. Tomlin

To sidestep the curse of dimensionality when computing solutions to Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an algorithm that leverages a neural network to approximate the value function.