Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using
Stochastic Scale

v1v2 (latest)

Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale

27 November 2023

Soyed Tuhin Ahmed

Michael Hefenbrock

ArXiv (abs)PDF HTML

Papers citing "Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale"

5 / 5 papers shown

Title
Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights Soyed Tuhin Ahmed Michael Hefenbrock M. Tahoori UQCV 119 2 0 07 May 2024
Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality Khadija Zanna Akane Sano FaML 163 4 0 12 Apr 2024
Scalable and Efficient Methods for Uncertainty Estimation and Reduction in Deep Learning Soyed Tuhin Ahmed BDL 99 0 0 13 Jan 2024
NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AIDesign, Automation and Test in Europe (DATE), 2024 Soyed Tuhin Ahmed K. Danouchi G. Prenat L. Anghel M. Tahoori 126 0 0 11 Jan 2024
Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural NetworksIEEE European Test Symposium (ETS), 2024 Soyed Tuhin Ahmed Michael Hefenbrock G. Prenat L. Anghel M. Tahoori 130 0 0 09 Jan 2024