UniStuttgartAI
@unistuttgartai.bsky.social
Official Bluesky account of the Institute for Artificial Intelligence, University of Stuttgart.
The aim of the newly founded institute is to research fundamental questions about AI, to reflect on the benefits for society and to promote the transfer of AI.
The aim of the newly founded institute is to research fundamental questions about AI, to reflect on the benefits for society and to promote the transfer of AI.
O. Mohammed, J. Pan, M. Nayyeri, D. Hernández, S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI 2025). arxiv.org/abs/2508.03251
Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Modeling evolving interactions among entities is critical in many real-world tasks. For example, predicting driver maneuvers in traffic requires tracking how neighboring vehicles accelerate, brake, an...
arxiv.org
August 12, 2025 at 8:52 AM
O. Mohammed, J. Pan, M. Nayyeri, D. Hernández, S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI 2025). arxiv.org/abs/2508.03251
Sadikaj, Y., Zhou, H., Halilaj, L., Schmid, S., Staab, S., & Plant, C. (2025). MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning. ICCV 2025. arxiv.org/abs/2504.06740
MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or not, it is crucial ...
arxiv.org
August 7, 2025 at 11:06 AM
Sadikaj, Y., Zhou, H., Halilaj, L., Schmid, S., Staab, S., & Plant, C. (2025). MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning. ICCV 2025. arxiv.org/abs/2504.06740
Fathallah, N., Hernández, D., & Staab, S. (2025). AccessGuru: Leveraging LLMs to detect and correct web accessibility violations in HTML code. ASSETS 2025. arxiv.org/abs/2507.19549
AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Code
The vast majority of Web pages fail to comply with established Web accessibility guidelines, excluding a range of users with diverse abilities from interacting with their content. Making Web pages acc...
arxiv.org
August 7, 2025 at 11:06 AM
Fathallah, N., Hernández, D., & Staab, S. (2025). AccessGuru: Leveraging LLMs to detect and correct web accessibility violations in HTML code. ASSETS 2025. arxiv.org/abs/2507.19549
🔗 See more details about the papers in our latest post: www.ki.uni-stuttgart.de/institute/ne...
Two Papers Addressing Real-World Problems Through AI to be presented at ICCV and ASSETS | News | Jul 29, 2025 | Institute for Artificial Intelligence | University of Stuttgart
www.ki.uni-stuttgart.de
August 7, 2025 at 11:06 AM
🔗 See more details about the papers in our latest post: www.ki.uni-stuttgart.de/institute/ne...
In particular, Dr. Stolle will introduce the model of “continuous safety engineering for high-risk AI systems,” which explicitly models uncertainties and confidences and propagates these during design time and operation time.
May 28, 2025 at 2:16 PM
In particular, Dr. Stolle will introduce the model of “continuous safety engineering for high-risk AI systems,” which explicitly models uncertainties and confidences and propagates these during design time and operation time.
Dr. Stolle will outline two perspectives on safety engineering for AI-enabled systems—a safety-centric view and an AI-centric view—and then present his team's approach of combining the best of these two worlds.
May 28, 2025 at 2:16 PM
Dr. Stolle will outline two perspectives on safety engineering for AI-enabled systems—a safety-centric view and an AI-centric view—and then present his team's approach of combining the best of these two worlds.
📍 NAACL 2025
Conformalized Answer Set Prediction for Knowledge Graph Embedding: arxiv.org/abs/2408.08248
Conformalized Answer Set Prediction for Knowledge Graph Embedding: arxiv.org/abs/2408.08248
Conformalized Answer Set Prediction for Knowledge Graph Embedding
Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The learned KG embeddings...
arxiv.org
April 24, 2025 at 10:06 PM
📍 NAACL 2025
Conformalized Answer Set Prediction for Knowledge Graph Embedding: arxiv.org/abs/2408.08248
Conformalized Answer Set Prediction for Knowledge Graph Embedding: arxiv.org/abs/2408.08248
📍 WWW 2025
DAGE: DAG Query Answering via Relational Combinator with Logical Constraints: arxiv.org/abs/2410.22105
MaSH: Maximal Separating Poincaré Hyperplanes for Hierarchical Imbalanced Learning: www.cs.emory.edu/~jyang71/fil...
DAGE: DAG Query Answering via Relational Combinator with Logical Constraints: arxiv.org/abs/2410.22105
MaSH: Maximal Separating Poincaré Hyperplanes for Hierarchical Imbalanced Learning: www.cs.emory.edu/~jyang71/fil...
DAGE: DAG Query Answering via Relational Combinator with Logical Constraints
Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this deco...
arxiv.org
April 24, 2025 at 10:06 PM
📍 WWW 2025
DAGE: DAG Query Answering via Relational Combinator with Logical Constraints: arxiv.org/abs/2410.22105
MaSH: Maximal Separating Poincaré Hyperplanes for Hierarchical Imbalanced Learning: www.cs.emory.edu/~jyang71/fil...
DAGE: DAG Query Answering via Relational Combinator with Logical Constraints: arxiv.org/abs/2410.22105
MaSH: Maximal Separating Poincaré Hyperplanes for Hierarchical Imbalanced Learning: www.cs.emory.edu/~jyang71/fil...
📍 ICLR 2025
From Tokens to Lattices: Emergent Lattice Structures in Language Models: arxiv.org/abs/2504.08778
From Tokens to Lattices: Emergent Lattice Structures in Language Models: arxiv.org/abs/2504.08778
From Tokens to Lattices: Emergent Lattice Structures in Language Models
Pretrained masked language models (MLMs) have demonstrated an impressive capability to comprehend and encode conceptual knowledge, revealing a lattice structure among concepts. This raises a critical ...
arxiv.org
April 24, 2025 at 10:06 PM
📍 ICLR 2025
From Tokens to Lattices: Emergent Lattice Structures in Language Models: arxiv.org/abs/2504.08778
From Tokens to Lattices: Emergent Lattice Structures in Language Models: arxiv.org/abs/2504.08778