1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2025
Assessing the Potential for Catastrophic Failure in Dynamic Post-Training Quantization
Logan Frank, Paul Ardis
Post-training quantization (PTQ) has recently emerged as an effective tool for reducing the computational complexity and memory usage of a neural network by representing its weight…
cs.LG2024★ 1 cited
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks
Paul Ardis, Arjuna Flenner
Deep Neural Networks (DNNs) do not inherently compute or exhibit empirically-justified task confidence. In mission critical applications, it is important to both understand associa…