Recurrent Relational Networks for Complex Relational Reasoning

By Rasmus Berg Palm, Ulrich Paquet, Ole Winther. 2018

31 citations recorded by the source corpus.

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Citations in the corpus

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, methodology): “The used methods may either be components that add additional capabilities, components that belong to the model architecture [75], [76], e.”
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (2018, background): “…about a speciﬁc environment, by introducing a relational reasoning module that learns a relational function, which can be applied to any DNN. Palm et al. (2017) build on work by Santoro et al. (2017) and introduces a recurrent relational network which can take the temporal component into…”
Learning a SAT Solver from Single-Bit Supervision (2018, background): “From the machine learning perspective, the closest work to ours is Palm et al. (2017), which showed that an MPNN can be trained to predict the unique solutions of Sudoku puzzles.”
Learning to Solve NP-Complete Problems - A Graph Neural Network for the Decision TSP (2018, background): “Gilmer et al., which apply it to quantum chemistry problems, adopt the term “neural message passing” (Gilmer et al. 2017), while Palm et al. refer to “recurrent relational networks” in an attempt to train neural networks to solve Sudoku puzzles (Palm et al. 2017).”
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver (2019, background): “We also show that we can learn to play 9 × 9 Sudoku, a problem that is challenging for modern neural network architectures (Palm et al., 2017).”