134 citations · 182 across the 13 of their papers we have counts for
3 papers · 1 filter
Greedy-layer Pruning: Speeding up Transformer Models for Natural Language Processing
David Peer, Sebastian Stabinger, Stefan Engl +1
Fine-tuning transformer models after unsupervised pre-training reaches a very high performance on many different natural language processing tasks. Unfortunately, transformers suff…
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…
Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks
Sebastian Stabinger, David Peer, Antonio Rodríguez-Sánchez
Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass huma…