3 papers
cs.LG2026
MASH-Bench: Diagnosing Cross-Source Failure in Mass-Shooting Risk Classification
Neha Sharma, Ritesh Sharma
Public mass-shooting databases differ substantially in coverage, feature availability, and reporting practices, creating challenges for machine-learning models that must generalize…
cs.CV2026
Architecture-Adaptive Uncertainty Fusion for Deepfake Detection
Ritesh Sharma, Mohammad Ghasemigol, Yuichi Motai
Deepfake detection systems achieve near-perfect accuracy on benchmarks, yet forensic deployment demands reliable prediction uncertainty. Existing uncertainty quantification (UQ) me…
cs.LG2026
When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice
Neha Sharma, Ritesh Sharma
We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets…