2 citations · 7 across the 6 of their papers we have counts for
6 papers
Beyond Fixed Tasks: Unsupervised Environment Design for Task-Level Pairs
Daniel Furelos-Blanco, Charles Pert, Frederik Kelbel +3
Training general agents to follow complex instructions (tasks) in intricate environments (levels) remains a core challenge in reinforcement learning. Random sampling of task-level…
Transformers Use Causal World Models in Maze-Solving Tasks
Alex F. Spies, William Edwards, Michael I. Ivanitskiy +5
Recent studies in interpretability have explored the inner workings of transformer models trained on tasks across various domains, often discovering that these networks naturally d…
Structured World Representations in Maze-Solving Transformers
Michael Igorevich Ivanitskiy, Alex F. Spies, Tilman Räuker +9
Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers. Given the siz…
A Configurable Library for Generating and Manipulating Maze Datasets
Michael Igorevich Ivanitskiy, Rusheb Shah, Alex F. Spies +8
Understanding how machine learning models respond to distributional shifts is a key research challenge. Mazes serve as an excellent testbed due to varied generation algorithms offe…
Sparse Relational Reasoning with Object-Centric Representations
Alex F. Spies, Alessandra Russo, Murray Shanahan
We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsi…
Nonlocal Thresholds for Improving the Spatial Resolution of Pixel Detectors
Benjamin Nachman, Alex Spies
Pixel detectors only record signals above a tuned threshold in order to suppress noise. As sensors become thinner, pitches decrease, and radiation damage reduces the collected char…