5 citations · 13 across the 8 of their papers we have counts for
8 papers
Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights
Soyed Tuhin Ahmed, Michael Hefenbrock, Mehdi B. Tahoori
The applications of artificial intelligence (AI) are rapidly evolving, and they are also commonly used in safety-critical domains, such as autonomous driving and medical diagnosis,…
Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs
Florentia Afentaki, Michael Hefenbrock, Georgios Zervakis +1
Printed Electronics (PE) stands out as a promisingtechnology for widespread computing due to its distinct attributes, such as low costs and flexible manufacturing. Unlike tradition…
Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
Soyed Tuhin Ahmed, Michael Hefenbrock, Guillaume Prenat +2
Bayesian Neural Networks (BayNNs) can inherently estimate predictive uncertainty, facilitating informed decision-making. Dropout-based BayNNs are increasingly implemented in spintr…
Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale
Soyed Tuhin Ahmed, Kamal Danouchi, Michael Hefenbrock +3
Uncertainty estimation in Neural Networks (NNs) is vital in improving reliability and confidence in predictions, particularly in safety-critical applications. Bayesian Neural Netwo…
Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation
Soyed Tuhin Ahmed, Kamal Danouchi, Michael Hefenbrock +3
Recently, machine learning systems have gained prominence in real-time, critical decision-making domains, such as autonomous driving and industrial automation. Their implementation…
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning
Yiran Huang, Yexu Zhou, Michael Hefenbrock +3
The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficia…
McXai: Local model-agnostic explanation as two games
Yiran Huang, Nicole Schaal, Michael Hefenbrock +4
To this day, a variety of approaches for providing local interpretability of black-box machine learning models have been introduced. Unfortunately, all of these methods suffer from…
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone
Yexu Zhou, Yuting Gao, Yiran Huang +3
An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method,…