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Found 8 papers, 0 papers with code
Precise-Physics Driven Text-to-3D Generation
However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for the generated 3D shapes.
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI.
ResLogit: A residual neural network logit model for data-driven choice modelling
This paper presents a novel deep learning-based travel behaviour choice model. Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model.
Information processing constraints in travel behaviour modelling: A generative learning approach
We propose a data-driven generative model version of rational inattention theory to emulate these behavioural representations.
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data
We systematically describe the proposed machine learning algorithm and develop a process of analyzing travel behaviour data from a generative learning perspective.
Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
We apply this framework on a mode choice survey data to identify abstract latent variables and compare the performance with a traditional latent variable model with specific latent preferences -- safety, comfort, and environmental.
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process.
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services.
