134 citations · 182 across the 13 of their papers we have counts for
17 papers
ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents
David Peer, Sebastian Stabinger
Large Language Models (LLMs) have demonstrated impressive capabilities, yet their deployment in high-stakes domains is hindered by inherent limitations in trustworthiness, includin…
ANLS* -- A Universal Document Processing Metric for Generative Large Language Models
David Peer, Philemon Schöpf, Volckmar Nebendahl +2
Traditionally, discriminative models have been the predominant choice for tasks like document classification and information extraction. These models make predictions that fall int…
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…
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…