Papers by Luc de Raedt
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments (2024.lrec-main)
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| Challenge: | Existing knowledge for reasoning about partially observed scenes is limited . lucian et al. show that pre-trained vision language models are not adequate for VQA . |
| Approach: | They propose a benchmark for reasoning-intensive visual question answering . they use logical constraints to leverage knowledge to generate plausible answers . |
| Outcome: | The proposed model performs better than pre-trained models on CLEVR-POC . the proposed model is based on a neuro-symbolic model with a visual perception network and a formal logical reasoner . |
Mapping probability word problems to executable representations (2021.emnlp-main)
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Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans
| Challenge: | a recent paper addresses the problem of solving math word problems automatically . a number of approaches have been proposed for solving word problems . |
| Approach: | They employ a sequence-to-sequence model to generate intermediate representations for word problems . they then use a probabilistic programming system to provide the answer . their best performing model incorporates general-domain contextualised word representations . |
| Outcome: | The proposed model is the best performing on a declarative language and a probabilistic programming system. |