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20242026
most citedRight on Time: Revising Time Series Models by Constraining their Explanations

1 citations · 3 across the 10 of their papers we have counts for

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cs.AI2024

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…

cs.LG2024

OCALM: Object-Centric Assessment with Language Models

Timo Kaufmann, Jannis Blüml, Antonia Wüst +3

Properly defining a reward signal to efficiently train a reinforcement learning (RL) agent is a challenging task. Designing balanced objective functions from which a desired behavi…

cs.AI2024★ 1 cited

Neural Concept Binder

Wolfgang Stammer, Antonia Wüst, David Steinmann +1

The challenge in object-based visual reasoning lies in generating concept representations that are both descriptive and distinct. Achieving this in an unsupervised manner requires…

cs.LG2024★ 1 cited

Right on Time: Revising Time Series Models by Constraining their Explanations

Maurice Kraus, David Steinmann, Antonia Wüst +2

Deep time series models often suffer from reliability issues due to their tendency to rely on spurious correlations, leading to incorrect predictions. To mitigate such shortcuts an…

cs.AI2024★ 1 cited

Pix2Code: Learning to Compose Neural Visual Concepts as Programs

Antonia Wüst, Wolfgang Stammer, Quentin Delfosse +2

The challenge in learning abstract concepts from images in an unsupervised fashion lies in the required integration of visual perception and generalizable relational reasoning. Mor…