Summary of Unexpected Benefits Of Self-modeling in Neural Systems, by Vickram N. Premakumar et al.
Unexpected Benefits of Self-Modeling in Neural Systems
by Vickram N. Premakumar, Michael Vaiana, Florin Pop, Judd Rosenblatt, Diogo Schwerz de Lucena, Kirsten Ziman, Michael S. A. Graziano
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Self-modeling artificial networks that learn to predict their internal states as an auxiliary task change fundamentally, becoming simpler, more regularized, and parameter-efficient. This restructuring effect reduces network complexity, making it more amenable to predictive modeling. By adding self-modeling to a range of network architectures performing three classification tasks across two modalities, we observed significant reductions in network complexity through narrower weight distributions and lower real log canonical thresholds (RLCT). The reduction became more pronounced as training weight was placed on the auxiliary task of self-modeling. These findings support the hypothesis that self-modeling is not just a network learning to predict itself but also has a restructuring effect, which may explain benefits reported in machine learning literature and shed light on social or cooperative contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial networks learn to predict their internal states as an auxiliary task, changing them in a fundamental way. This change makes the networks simpler, more regularized, and more efficient with parameters. To test this idea, we used different network types performing three tasks across two ways of processing information. When the networks learned to predict themselves, they became less complex in two ways: their weights were spread out less and a measure called real log canonical threshold (RLCT) was lower. The amount of complexity reduction increased as the networks focused more on this self-predicting task. This discovery supports the idea that self-modeling is not just about the network learning itself but also has an impact on its structure, which might help explain why self-models are beneficial in certain situations. |
Keywords
» Artificial intelligence » Classification » Machine learning » Parameter efficient
