Learning Abstract Classes using Deep Learning. - Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez; Justus H. Piater (BICT, 2015)

The Effects of Social Gaze in Human-Robot Collaborative Assembly. - Kerstin Fischer; Lars Christian Jensen; Franziska Kirstein; Sebastian Stabinger; Özgür Erkent; Dadhichi Shukla; Justus H. Piater (ICSR, 2015)

25 Years of CNNs: Can We Compare to Human Abstraction Capabilities? - Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez; Justus H. Piater (ICANN (2), 2016)

Monocular obstacle avoidance for blind people using probabilistic focus of expansion estimation. - Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez; Justus H. Piater (WACV, 2016)

Evaluation of Deep Learning on an Abstract Image Classification Dataset. - Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (ICCV Workshops, 2017)

A deep learning approach for detecting and correcting highlights in endoscopic images. - Antonio Jose Rodríguez-Sánchez; Daly Chea; George Azzopardi; Sebastian Stabinger (IPTA, 2017)

Autonomous skill-centric testing using deep learning. - Simon Hangl; Sebastian Stabinger; Justus H. Piater (IROS, 2017)

Guided Labeling using Convolutional Neural Networks. - Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (CoRR, 2017)

Training Deep Capsule Networks. - David Peer; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (CoRR, 2018)

Evaluating CNNs on the Gestalt Principle of Closure. - Gregor Ehrensperger; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (ICANN (1), 2019)

Limitations of routing-by-agreement based capsule networks. - David Peer; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (CoRR, 2019)

Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification. - Alexander Rietzler; Sebastian Stabinger; Paul Opitz; Stefan Engl (LREC, 2020)

Evaluating the Progress of Deep Learning for Visual Relational Concepts. - Sebastian Stabinger; Justus H. Piater; Antonio Jose Rodríguez-Sánchez (CoRR, 2020)

Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football. - Bernhard Hollaus; Sebastian Stabinger; Andreas H. Mehrle; Christian Raschner (Sensors, 2020)

Adversarial Examples Against a BERT ABSA Model - Fooling Bert With L33T, Misspellign, and Punctuation, . - Nora Hofer; Pascal Schöttle; Alexander Rietzler; Sebastian Stabinger (ARES, 2021)

Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks. - David Peer; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (WACV, 2021)

Auto-tuning of Deep Neural Networks by Conflicting Layer Removal. - David Peer; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (CoRR, 2021)

Greedy Layer Pruning: Decreasing Inference Time of Transformer Models. - David Peer; Sebastian Stabinger; Stefan Engl; Antonio Jose Rodríguez-Sánchez (CoRR, 2021)

Arguments for the unsuitability of convolutional neural networks for non-local tasks. - Sebastian Stabinger; David Peer; Antonio Jose Rodríguez-Sánchez (Neural Networks, 2021)

Limitation of capsule networks. - David Peer; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (Pattern Recognit. Lett., 2021)

Classification of Tennis Shots with a Neural Network Approach. - Andreas Ganser; Bernhard Hollaus; Sebastian Stabinger (Sensors, 2021)

conflicting_bundle.py - A python module to identify problematic layers in deep neural networks. - David Peer; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (Softw. Impacts, 2021)

Improving 3D Point Cloud Reconstruction with Dynamic Tree-Structured Capsules. - Chris Engelhardt; Jakob Mittelberger; David Peer; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (IPAS, 2022)

Evaluating Attention in Convolutional Neural Networks for Blended Images. - Andrea Portscher; Sebastian Stabinger; Antonio Jose Rodríguez-Sánchez (IPAS, 2022)

Greedy-layer pruning: Speeding up transformer models for natural language processing. - David Peer; Sebastian Stabinger; Stefan Engl; Antonio Jose Rodríguez-Sánchez (Pattern Recognit. Lett., 2022)

Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization. - David Peer; Bart Keulen; Sebastian Stabinger; Justus H. Piater; Antonio Jose Rodríguez-Sánchez (Trans. Mach. Learn. Res., 2022)

ANLS* - A Universal Document Processing Metric for Generative Large Language Models. - David Peer; Philemon Schöpf; Volckmar Nebendahl; Alexander Rietzler; Sebastian Stabinger (CoRR, 2024)