4 citations · 5 across the 9 of their papers we have counts for
5 papers · 2 filters
Uncertainty-Preserving QBNNs: Multi-Level Quantization of SVI-Based Bayesian Neural Networks for Image Classification
Hendrik Borras, Yong Wu, Bernhard Klein +1
Bayesian Neural Networks (BNNs) provide principled uncertainty quantification but suffer from substantial computational and memory overhead compared to deterministic networks. Whil…
Temporal speckle enables probabilistic weights for uncertainty-aware photonic AI
F. Brückerhoff-Plückelmann, H. Borras, S. U. Hulyal +12
Artificial intelligence increasingly guides decisions in safety-critical settings, yet neural networks can remain overconfident when confronted with ambiguous observations or unfam…
Accelerated Execution of Bayesian Neural Networks using a Single Probabilistic Forward Pass and Code Generation
Bernhard Klein, Falk Selker, Hendrik Borras +3
Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handling restricts use in s…
Variance-Aware Noisy Training: Hardening DNNs against Unstable Analog Computations
Xiao Wang, Hendrik Borras, Bernhard Klein +1
The disparity between the computational demands of deep learning and the capabilities of compute hardware is expanding drastically. Although deep learning achieves remarkable perfo…
On Hardening DNNs against Noisy Computations
Xiao Wang, Hendrik Borras, Bernhard Klein +1
The success of deep learning has sparked significant interest in designing computer hardware optimized for the high computational demands of neural network inference. As further mi…