134 citations · 182 across the 13 of their papers we have counts for
5 papers · 1 filter
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization
David Peer, Bart Keulen, Sebastian Stabinger +2
Training deep neural networks is a very demanding task, especially challenging is how to adapt architectures to improve the performance of trained models. We can find that sometime…
Auto-tuning of Deep Neural Networks by Conflicting Layer Removal
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this pa…
Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this pa…
Limitation of capsule networks
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the outpu…
Increasing the adversarial robustness and explainability of capsule networks with -capsules
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
In this paper we introduce a new inductive bias for capsule networks and call networks that use this prior -capsule networks. Our inductive bias that is inspired by TE neurons o…