Imagen de Portada

Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Rafe, Amir, Singleton, Patrick A.
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Computers and Society
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach
por: Rafe, Amir, et al.
Publicado: (2023)

Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery
por: Rafe, Amir, et al.
Publicado: (2026)

Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection
por: Castellani, Andrea, et al.
Publicado: (2025)

Predicting delays in Indian lower courts using AutoML and Decision Forests
por: Bhatnagar, Mohit, et al.
Publicado: (2023)

Crash Severity Risk Modeling Strategies under Data Imbalance
por: Mamun, Abdullah Al, et al.
Publicado: (2024)

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
por: Trirat, Patara, et al.
Publicado: (2024)

Assessing the Role of Intersection Proximity in Pedestrian Crashes: Insights from Data Mining Approach
por: Hossain, Ahmed, et al.
Publicado: (2026)

OpenAutoNLU: Open Source AutoML Library for NLU
por: Arshinov, Grigory, et al.
Publicado: (2026)

In-Context Decision Making for Optimizing Complex AutoML Pipelines
por: Balef, Amir Rezaei, et al.
Publicado: (2025)

Exploring the impact of fairness-aware criteria in AutoML
por: Simões, Joana, et al.
Publicado: (2026)

Optimal Pricing for Data-Augmented AutoML Marketplaces
por: Han, Minbiao, et al.
Publicado: (2023)

NNGPT: Rethinking AutoML with Large Language Models
por: Kochnev, Roman, et al.
Publicado: (2025)

Problem-oriented AutoML in Clustering
por: da Silva, Matheus Camilo, et al.
Publicado: (2024)

The Potential of AutoML for Recommender Systems
por: Vente, Tobias, et al.
Publicado: (2024)

SubStrat: A Subset-Based Strategy for Faster AutoML
por: Lazebnik, Teddy, et al.
Publicado: (2022)

AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
por: Tornede, Alexander, et al.
Publicado: (2023)

Vehicle-group-based Crash Risk Prediction and Interpretation on Highways
por: Zhu, Tianheng, et al.
Publicado: (2024)

UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
por: Guo, Jiayi, et al.
Publicado: (2024)

X Hacking: The Threat of Misguided AutoML
por: Sharma, Rahul, et al.
Publicado: (2024)

Robustness of AutoML on Dirty Categorical Data
por: Bueno, Marcos L. P., et al.
Publicado: (2026)

An AutoML-based approach for Network Intrusion Detection
por: Gyimah, Nana Kankam, et al.
Publicado: (2024)

Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization
por: Kaplan, Ahmet
Publicado: (2026)

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
por: Weerts, Hilde, et al.
Publicado: (2023)

LEMUR Neural Network Dataset: Towards Seamless AutoML
por: Goodarzi, Arash Torabi, et al.
Publicado: (2025)

Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
por: da Silva, Matheus Camilo, et al.
Publicado: (2026)

AutoML Benchmark with shorter time constraints and early stopping
por: Jurado, Israel Campero, et al.
Publicado: (2025)

Hyperparameter Importance Analysis for Multi-Objective AutoML
por: Theodorakopoulos, Daphne, et al.
Publicado: (2024)

Budget-aware Query Tuning: An AutoML Perspective
por: Wu, Wentao, et al.
Publicado: (2024)

LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data
por: Lapin, Aleksey, et al.
Publicado: (2025)

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
por: Tang, Zhiqiang, et al.
Publicado: (2024)

AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models
por: Lee, Sangjun, et al.
Publicado: (2025)

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
por: Tang, Zhiqiang, et al.
Publicado: (2024)

Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Fees
por: Kumar, Avishek, et al.
Publicado: (2023)

EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines
por: Simões, Joana, et al.
Publicado: (2025)

Online Meta-learning for AutoML in Real-time (OnMAR)
por: Gerber, Mia, et al.
Publicado: (2025)

Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making
por: Kozak, Anna, et al.
Publicado: (2024)

AutoML for Multi-Class Anomaly Compensation of Sensor Drift
por: Schaller, Melanie, et al.
Publicado: (2025)

Assessing the Use of AutoML for Data-Driven Software Engineering
por: Calefato, Fabio, et al.
Publicado: (2023)

ML Interpretability: Simple Isn't Easy
por: Räz, Tim
Publicado: (2022)

forester: A Tree-Based AutoML Tool in R
por: Ruczyński, Hubert, et al.
Publicado: (2024)