mlsanityData to 14 Oct 2026

Reasoning-Modulated Representations

arXiv 2107.08881 ↗First posted 19 Jul 2021cs.LG cs.AI stat.MLcited by 13

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Abstract (arXiv)

Neural networks leverage robust internal representations in order to generalise. Learning them is difficult, and often requires a large training set that covers the data distribution densely. We study a common setting where our task is not purely opaque. Indeed, very often we may have access to information about the underlying system (e.g. that observations must obey certain laws of physics) that any "tabula rasa" neural network would need to re-learn from scratch, penalising performance. We incorporate this information into a pre-trained reasoning module, and investigate its role in shaping the discovered representations in diverse self-supervised learning settings from pixels. Our approach paves the way for a new class of representation learning, grounded in algorithmic priors.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 46 of its 61 references that are in the map. First posted on arXiv 19 Jul 2021.

Builds on

46 of 61 references.

References · 46

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34 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 4; 2025: 1; 2026: 1.

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