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cs.LG20264 cited

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba

Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi +5

Structured State Space Models (SSMs) have become a prominent class of sequence models, developed against two long-standing difficulties: the sequential computation and gradient pro…

cs.LG2026

Characterizing and Predicting Wildfire Evacuation Behavior: A Dual-Stage ML Approach

Sazzad Bin Bashar Polock, Anandi Dutta, Subasish Das

Wildfire evacuation behavior is highly variable and influenced by complex interactions among household resources, preparedness, and situational cues. Using a large-scale MTurk surv…

cs.LG2025

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…

cs.LG2025

Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes

Shriyank Somvanshi, Anannya Ghosh Tusti, Mahmuda Sultana Mimi +4

The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved i…

cs.LG2025

Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet

Shriyank Somvanshi, Rohit Chakraborty, Subasish Das +1

Child bicyclists (14 years and younger) are among the most vulnerable road users, often experiencing severe injuries or fatalities in crashes. This study analyzed 2,394 child bicyc…