Cover Image

Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Somvanshi, Shriyank, Tusti, Anannya Ghosh, Chakraborty, Rohit, Das, Subasish
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity
by: Mimi, Mahmuda Sultana, et al.
Published: (2025)

Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes
by: Somvanshi, Shriyank, et al.
Published: (2025)

Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet
by: Somvanshi, Shriyank, et al.
Published: (2025)

Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
by: Somvanshi, Shriyank, et al.
Published: (2025)

A Survey on Deep Tabular Learning
by: Somvanshi, Shriyank, et al.
Published: (2024)

A Survey on Kolmogorov-Arnold Network
by: Somvanshi, Shriyank, et al.
Published: (2024)

From Tiny Machine Learning to Tiny Deep Learning: A Survey
by: Somvanshi, Shriyank, et al.
Published: (2025)

A Dimensionality-Reduced XAI Framework for Roundabout Crash Severity Insights
by: Chakraborty, Rohit, et al.
Published: (2025)

From Maneuver to Mishap: A Systematic Literature Review on U-Turn Safety Risks
by: Javed, Syed Aaqib, et al.
Published: (2025)

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State- Space Architectures from S4 to Mamba
by: Somvanshi, Shriyank, et al.
Published: (2025)

Driving Education Advancements of Novice Drivers: A Systematic Literature Review
by: Tusti, Anannya Ghosh, et al.
Published: (2025)

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

Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
by: Antariksa, Gian, et al.
Published: (2025)

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
by: Chhetri, Gaurab, et al.
Published: (2025)

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

A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs
by: Somvanshi, Shriyank, et al.
Published: (2025)

In-Context Learning in Linear vs. Quadratic Attention Models: An Empirical Study on Regression Tasks
by: Goel, Ayush, et al.
Published: (2026)

Improving Deep Tabular Learning
by: Sarafian, Sivan, et al.
Published: (2025)

Mambular: A Sequential Model for Tabular Deep Learning
by: Thielmann, Anton Frederik, et al.
Published: (2024)

Characterizing and Predicting Wildfire Evacuation Behavior: A Dual-Stage ML Approach
by: Polock, Sazzad Bin Bashar, et al.
Published: (2026)

Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
by: Lares, Oscar, et al.
Published: (2024)

A Generative Deep Learning Approach for Crash Severity Modeling with Imbalanced Data
by: Chen, Junlan, et al.
Published: (2024)

Predictive Crash Analytics for Traffic Safety using Deep Learning
by: Sivakoti, Karthik
Published: (2025)

Benchmarking Optimizers for MLPs in Tabular Deep Learning
by: Gorishniy, Yury, et al.
Published: (2026)

Model Context Protocols in Adaptive Transport Systems: A Survey
by: Chhetri, Gaurab, et al.
Published: (2025)

Amortized Inference for Model Rocket Aerodynamics: Learning to Estimate Physical Parameters from Simulation
by: Pandey, Rohit, et al.
Published: (2025)

Tabular and Deep Learning for the Whittle Index
by: Relaño, Francisco Robledo, et al.
Published: (2024)

Boosting Relational Deep Learning with Pretrained Tabular Models
by: Lachi, Veronica, et al.
Published: (2025)

TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
by: Simonetto, Thibault, et al.
Published: (2024)

Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
by: Yang, Zeyu, et al.
Published: (2024)

Tabular and Deep Reinforcement Learning for Gittins Index
by: Dhankhar, Harshit, et al.
Published: (2024)

Unveiling the Role of Data Uncertainty in Tabular Deep Learning
by: Kartashev, Nikolay, et al.
Published: (2025)

Iterative Feature Exclusion Ranking for Deep Tabular Learning
by: Shaninah, Fathi Said Emhemed, et al.
Published: (2024)

On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
by: Thielmann, Anton Frederik, et al.
Published: (2024)

Neural-g: A Deep Learning Framework for Mixing Density Estimation
by: Wang, Shijie, et al.
Published: (2024)

Convex Clustering Redefined: Robust Learning with the Median of Means Estimator
by: De, Sourav, et al.
Published: (2025)

RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction
by: Zhu, Di, et al.
Published: (2025)

Crash Severity Prediction Using Deep Learning Approaches: A Hybrid CNN-RNN Framework
by: Koohfar, Sahar
Published: (2025)

Deep Tabular Representation Corrector
by: Ye, Hangting, et al.
Published: (2026)

Towards Benchmarking Foundation Models for Tabular Data With Text
by: Mráz, Martin, et al.
Published: (2025)