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Found 3 papers, 2 papers with code
Structured World Representations in Maze-Solving Transformers
Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers.
A Configurable Library for Generating and Manipulating Maze Datasets
Understanding how machine learning models respond to distributional shifts is a key research challenge.
Sparse Relational Reasoning with Object-Centric Representations
We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsity-inducing constraints.
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