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- Centre National de la Recherche ScientifiqueFR17 papers
- Space Telescope Science InstituteUS14 papers
- California Institute of TechnologyUS11 papers
- Leiden UniversityNL11 papers
- Max Planck Institute for AstronomyDE11 papers
- University of MichiganUS11 papers
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- The University of Texas at AustinUS10 papers
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- University of HoustonUS9 papers
- Center for Astrophysics Harvard & SmithsonianUS8 papers
5 papers · 2 filters
Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
Shriyank Somvanshi, Pavan Hebli, Gaurab Chhetri +1
This study presents a deep tabular learning framework for predicting crash severity in electric vehicle (EV) collisions using real-world crash data from Texas (2017-2023). After fi…
Evaluating Pavement Deterioration Rates Due to Flooding Events Using Explainable AI
Lidan Peng, Lu Gao, Feng Hong +1
Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events aff…
From Tiny Machine Learning to Tiny Deep Learning: A Survey
Shriyank Somvanshi, Md Monzurul Islam, Gaurab Chhetri +8
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counte…
ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity
Mahmuda Sultana Mimi, Md Monzurul Islam, Anannya Ghosh Tusti +2
Understanding the spatial and temporal dynamics of automated vehicle (AV) crash severity is critical for advancing urban mobility safety and infrastructure planning. In this work,…
Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification
Arshia Kermani, Ehsan Zeraatkar, Habib Irani
The increasing computational demands of transformer models in time series classification necessitate effective optimization strategies for energy-efficient deployment. Our study pr…