:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Feng, Siqi, Yao, Rui, Hess, Stephane, Daziano, Ricardo A., Brathwaite, Timothy, Walker, Joan, Wang, Shenhao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2404.14701
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
by: Wang, Shenhao, et al.
Published: (2021)

Graph neural networks for residential location choice: connection to classical logit models
by: Cheng, Zhanhong, et al.
Published: (2025)

Bayesian Deep Learning for Discrete Choice
by: Villarraga, Daniel F., et al.
Published: (2025)

Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
by: Wang, Qingyi, et al.
Published: (2023)

Designing Graph Convolutional Neural Networks for Discrete Choice with Network Effects
by: Villarraga, Daniel F., et al.
Published: (2025)

Statistical significance in choice modelling: computation, usage and reporting
by: Hess, Stephane, et al.
Published: (2025)

Implicit regularization of deep residual networks towards neural ODEs
by: Marion, Pierre, et al.
Published: (2023)

Exploring the loss landscape of regularized neural networks via convex duality
by: Kim, Sungyoon, et al.
Published: (2024)

Delphos: A reinforcement learning framework for assisting discrete choice model specification
by: Nova, Gabriel, et al.
Published: (2025)

Method for noise-induced regularization in quantum neural networks
by: Kuzmin, Viacheslav, et al.
Published: (2024)

Adversarially robust generalization theory via Jacobian regularization for deep neural networks
by: Wu, Dongya, et al.
Published: (2024)

SGD method for entropy error function with smoothing l0 regularization for neural networks
by: Nguyen, Trong-Tuan, et al.
Published: (2024)

Beyond topography: Topographic regularization improves robustness and reshapes representations in convolutional neural networks
by: Truong, Nhut, et al.
Published: (2025)

Additive regularization schedule for neural architecture search
by: Potanin, Mark, et al.
Published: (2024)

Symmetry-regularized neural ordinary differential equations
by: Hao, Wenbo
Published: (2023)

Deep linear networks for regression are implicitly regularized towards flat minima
by: Marion, Pierre, et al.
Published: (2024)

Incorporating graph neural network into route choice model
by: Ma, Yuxun, et al.
Published: (2025)

Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
by: Schotthöfer, Steffen, et al.
Published: (2024)

A multiobjective continuation method to compute the regularization path of deep neural networks
by: Amakor, Augustina C., et al.
Published: (2023)

Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow
by: Li, Qin, et al.
Published: (2024)

Improved Physics-informed neural networks loss function regularization with a variance-based term
by: Hanna, John M., et al.
Published: (2024)

The duality structure gradient descent algorithm: analysis and applications to neural networks
by: Flynn, Thomas
Published: (2017)

Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown
by: Okuno, Akifumi, et al.
Published: (2023)

Input-gradient space particle inference for neural network ensembles
by: Trinh, Trung, et al.
Published: (2023)

Small noise analysis for Tikhonov and RKHS regularizations
by: Lang, Quanjun, et al.
Published: (2023)

Convergence of gradient flow for learning convolutional neural networks
by: Diederen, Jona-Maria, et al.
Published: (2026)

Energy stable neural network for gradient flow equations
by: Wu, Yue, et al.
Published: (2023)

Saving for the future: Enhancing generalization via partial logic regularization
by: Tan, Zhaorui, et al.
Published: (2025)

Linear regression with overparameterized linear neural networks: Tight upper and lower bounds for implicit $\ell^1$-regularization
by: Matt, Hannes, et al.
Published: (2025)

Integral regularization PINNs for evolution equations
by: Feng, Xiaodong, et al.
Published: (2025)

Topological safeguard for evasion attack interpreting the neural networks' behavior
by: Echeberria-Barrio, Xabier, et al.
Published: (2024)

On the regularization of Wasserstein GANs
by: Petzka, Henning, et al.
Published: (2017)

An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks
by: Hernandez, Jose Ignacio, et al.
Published: (2024)

Wasserstein-regularized Conformal Prediction under General Distribution Shift
by: Xu, Rui, et al.
Published: (2025)

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
by: Qu, Gang, et al.
Published: (2021)

Improved physics-informed neural network in mitigating gradient related failures
by: Niu, Pancheng, et al.
Published: (2024)

Debiased neural operators for estimating functionals
by: Hess, Konstantin, et al.
Published: (2026)

Statistical analysis of Inverse Entropy-regularized Reinforcement Learning
by: Belomestny, Denis, et al.
Published: (2025)

Recurrent neural networks: vanishing and exploding gradients are not the end of the story
by: Zucchet, Nicolas, et al.
Published: (2024)

Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network
by: Zhuang, Dingyi, et al.
Published: (2024)