148 citations · 261 across the 19 of their papers we have counts for
6 papers · 1 filter
Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?
Antonia Wüst, Tim Woydt, Lukas Helff +5
Recently, newly developed Vision-Language Models (VLMs), such as OpenAI's o1, have emerged, seemingly demonstrating advanced reasoning capabilities across text and image modalities…
Learning by Self-Explaining
Wolfgang Stammer, Felix Friedrich, David Steinmann +3
Much of explainable AI research treats explanations as a means for model inspection. Yet, this neglects findings from human psychology that describe the benefit of self-explanation…
Neural Meta-Symbolic Reasoning and Learning
Zihan Ye, Hikaru Shindo, Devendra Singh Dhami +1
Deep neural learning uses an increasing amount of computation and data to solve very specific problems. By stark contrast, human minds solve a wide range of problems using a fixed…
LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems
Björn Deiseroth, Patrick Schramowski, Hikaru Shindo +2
Text-to-image models have recently achieved remarkable success with seemingly accurate samples in photo-realistic quality. However as state-of-the-art language models still struggl…
Relational Linear Programs
Kristian Kersting, Martin Mladenov, Pavel Tokmakov
We propose relational linear programming, a simple framework for combing linear programs (LPs) and logic programs. A relational linear program (RLP) is a declarative LP template de…
Counting Belief Propagation
Kristian Kersting, Babak Ahmadi, Sriraam Natarajan
A major benefit of graphical models is that most knowledge is captured in the model structure. Many models, however, produce inference problems with a lot of symmetries not reflect…