Theory-based residual neural networks: A synergy of discrete choice
models and deep neural networks

Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks

22 October 2020

Shenhao Wang

Papers citing "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks"

9 / 9 papers shown

Title
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization Siqi Feng Rui Yao Stephane Hess Ricardo A. Daziano Timothy Brathwaite Joan Walker Shenhao Wang 30 1 0 23 Apr 2024
Deep trip generation with graph neural networks for bike sharing system expansion Yuebing Liang Fangyi Ding Guan Huang Zhan Zhao AI4TS 29 15 0 20 Mar 2023
Fairness-enhancing deep learning for ride-hailing demand prediction Yunhan Zheng Qingyi Wang Dingyi Zhuang Shenhao Wang Jinhua Zhao 41 12 0 10 Mar 2023
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis? Qingyi Wang Shenhao Wang Yunhan Zheng Hongzhou Lin Xiaohu Zhang Jinhua Zhao Joan Walker 13 9 0 07 Mar 2023
A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling Rui Yao S. Bekhor DRL 16 13 0 19 Jun 2021
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,696 0 28 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,113 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 317 5,847 0 08 Jul 2016