241 citations · 275 across the 16 of their papers we have counts for
17 papers
Zero-Shot Object Removal via Attention Masking, Latent Anchoring, and Refinement
Arman Taghizadeh, Ulf Krumnack, Kai-Uwe Kühnberger
Removing an object from a real image requires more than synthesizing plausible content within a mask: the method must suppress residual object features, preserve the unedited scene…
Mapping and Classification of Trees Outside Forests using Deep Learning
Moritz Lucas, Hamid Ebrahimy, Viacheslav Barkov +3
Trees Outside Forests (TOF) play an important role in agricultural landscapes by supporting biodiversity, sequestering carbon, and regulating microclimates. Yet, most studies have…
Enhancing SLM via ChatGPT and Dataset Augmentation
Tom Pieper, Mohamad Ballout, Ulf Krumnack +2
This paper explores the enhancement of small language models through strategic dataset augmentation via ChatGPT-3.5-Turbo, in the domain of Natural Language Inference (NLI). By emp…
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Enhancing small language models for real-life application deployment is a significant challenge facing the research community. Due to the difficulties and costs of using large lang…
Show Me How It's Done: The Role of Explanations in Fine-Tuning Language Models
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Our research demonstrates the significant benefits of using fine-tuning with explanations to enhance the performance of language models. Unlike prompting, which maintains the model…
Investigating Pre-trained Language Models on Cross-Domain Datasets, a Step Closer to General AI
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Pre-trained language models have recently emerged as a powerful tool for fine-tuning a variety of language tasks. Ideally, when models are pre-trained on large amount of data, they…
Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language Tasks
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Investigating deep learning language models has always been a significant research area due to the ``black box" nature of most advanced models. With the recent advancements in pre-…
Grounding Psychological Shape Space in Convolutional Neural Networks
Lucas Bechberger, Kai-Uwe Kühnberger
Shape information is crucial for human perception and cognition, and should therefore also play a role in cognitive AI systems. We employ the interdisciplinary framework of concept…
Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised
Viviane Clay, Peter König, Gordon Pipa +1
Most artificial neural networks used for object detection and recognition are trained in a fully supervised setup. This is not only very resource consuming as it requires large dat…
Generalizing Psychological Similarity Spaces to Unseen Stimuli
Lucas Bechberger, Kai-Uwe Kühnberger
The cognitive framework of conceptual spaces proposes to represent concepts as regions in psychological similarity spaces. These similarity spaces are typically obtained through mu…
Formal Ways for Measuring Relations between Concepts in Conceptual Spaces
Lucas Bechberger, Kai-Uwe Kühnberger
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concep…
Formalized Conceptual Spaces with a Geometric Representation of Correlations
Lucas Bechberger, Kai-Uwe Kühnberger
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are…
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Tarek R. Besold, Artur d'Avila Garcez, Sebastian Bader +11
The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several…
A Comprehensive Implementation of Conceptual Spaces
Lucas Bechberger, Kai-Uwe Kühnberger
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points and concepts are represented by region…
Measuring Relations Between Concepts In Conceptual Spaces
Lucas Bechberger, Kai-Uwe Kühnberger
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concep…
A Thorough Formalization of Conceptual Spaces
Lucas Bechberger, Kai-Uwe Kühnberger
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concep…
Towards Grounding Conceptual Spaces in Neural Representations
Lucas Bechberger, Kai-Uwe Kühnberger
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. It aims at bridging the gap between symbolic and subsymbolic processing. I…