Explanipedia

Modeling spatial distribution of particles in transportation systems using computational fluid dynamics and machine learning approaches Open

Zeinab Bahman Zadeh, James Lo · 2024

This research presents a framework for modeling and predicting the spatial distribution of particle concentrations in public transportation systems, specifically buses and trains, using a combination of Computational Fluid Dynamics (CFD) s…

Replication Data for: A Non-parametric Bayesian Model for Detecting Differential Item Functioning: An Application to Political Representation in the US Open

Yuki Shiraito, James Lo, Santiago Olivella · 2022

Computer science Psychology Mathematics

A common approach when studying the quality of representation involves comparing the latent preferences of voters and legislators, commonly obtained by fitting an item-response theory (IRT) model to a common set of stimuli. Despite being e…

A Non-parametric Bayesian Model for Detecting Differential Item Functioning: An Application to Political Representation in the US Open

Yuki Shiraito, James Lo, Santiago Olivella · 2022

Computer science Psychology Mathematics

A common approach when studying the quality of representation involves comparing the latent preferences of voters and legislators, commonly obtained by fitting an item-response theory (IRT) model to a common set of stimuli. Despite being e…

Adaptively Solving the Local-Minimum Problem for Deep Neural Networks Open

Huachuan Wang, James Lo · 2021

Computer science Mathematics Economics

This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss…

Adaptively Solving the Local-Minimum Problem for Deep Neural Networks Open

Huachuan Wang, James Lo · 2020

Computer science Mathematics Physics

This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss…

Low-Order Model of Biological Neural Networks Open

Huachuan Wang, James Lo · 2020

Computer science Mathematics Physics

A biologically plausible low-order model (LOM) of biological neural networks is a recurrent hierarchical network of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/ supervised covariance/accumulative learning mechanisms, fe…

Replication Data for: Robustness of Empirical Evidence for the Democratic Peace Open