1 citations · 4 across the 23 of their papers we have counts for
5 papers · 1 filter
Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models
Björn Deiseroth, Max Henning Höth, Kristian Kersting +1
Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic rather than verifiable evidence -- le…
Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining
Rupert Mitchell, Kristian Kersting
Pretraining transformers on long sequences (entire code repositories, collections of related documents) is bottlenecked by quadratic attention costs. We present Multipole Semantic…
Learning from Less: Guiding Deep Reinforcement Learning with Differentiable Symbolic Planning
Zihan Ye, Oleg Arenz, Kristian Kersting
When tackling complex problems, humans naturally break them down into smaller, manageable subtasks and adjust their initial plans based on observations. For instance, if you want t…
Object Centric Concept Bottlenecks
David Steinmann, Wolfgang Stammer, Antonia Wüst +1
Developing high-performing, yet interpretable models remains a critical challenge in modern AI. Concept-based models (CBMs) attempt to address this by extracting human-understandab…
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…