9 citations
5 papers
Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks
Laura Fieback, Bidya Dash, Jakob Spiegelberg +1
Quantifying predictive uncertainty of deep semantic segmentation networks is essential in safety-critical tasks. In applications like autonomous driving, where video data is availa…
Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison
Lukas Brunke, Prateek Agrawal, Nikhil George
Input perturbation methods occlude parts of an input to a function and measure the change in the function's output. Recently, input perturbation methods have been applied to genera…
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Justin Bayer, Maximilian Soelch, Atanas Mirchev +2
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often on…
Assessing Solution Quality of 3SAT on a Quantum Annealing Platform
Thomas Gabor, Sebastian Zielinski, Sebastian Feld +6
When solving propositional logic satisfiability (specifically 3SAT) using quantum annealing, we analyze the effect the difficulty of different instances of the problem has on the q…
Dynamic Programming on Nominal Graphs
Nicklas Hoch, Ugo Montanari, Matteo Sammartino
Many optimization problems can be naturally represented as (hyper) graphs, where vertices correspond to variables and edges to tasks, whose cost depends on the values of the adjace…