20 citations · 36 across the 18 of their papers we have counts for
6 papers · 1 filter
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles
Sophie Steger, Christian Knoll, Bernhard Klein +2
Bayesian inference in function space has gained attention due to its robustness against overparameterization in neural networks. However, approximating the infinite-dimensional fun…
Less Memory Means smaller GPUs: Backpropagation with Compressed Activations
Daniel Barley, Holger Fröning
The ever-growing scale of deep neural networks (DNNs) has lead to an equally rapid growth in computational resource requirements. Many recent architectures, most prominently Large…
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems
Franz Kevin Stehle, Wainer Vandelli, Giuseppe Avolio +2
Anomaly detection in distributed systems such as High-Performance Computing (HPC) clusters is vital for early fault detection, performance optimisation, security monitoring, reliab…
GraphMatch: Subgraph Query Processing on FPGAs
Jonas Dann, Tobias Götz, Daniel Ritter +2
Efficiently finding subgraph embeddings in large graphs is crucial for many application areas like biology and social network analysis. Set intersections are the predominant and mo…
Probabilistic Photonic Computing with Chaotic Light
Frank Brückerhoff-Plückelmann, Hendrik Borras, Bernhard Klein +10
Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data, a task that poses significant challenges…
Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification
Yannick Emonds, Kai Xi, Holger Fröning
Resistive memory is a promising alternative to SRAM, but is also an inherently unstable device that requires substantial effort to ensure correct read and write operations. To avoi…