Computer Science > Neural and Evolutionary Computing
arXiv:2305.08746 (cs)
[Submitted on 4 May 2023 (v1), last revised 6 Jun 2023 (this version, v3)]
Title:Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
View a PDF of the paper titled Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability, by Ziming Liu and 2 other authors
View PDFAbstract:We introduce Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. Inspired by brains, BIMT embeds neurons in a geometric space and augments the loss function with a cost proportional to the length of each neuron connection. We demonstrate that BIMT discovers useful modular neural networks for many simple tasks, revealing compositional structures in symbolic formulas, interpretable decision boundaries and features for classification, and mathematical structure in algorithmic datasets. The ability to directly see modules with the naked eye can complement current mechanistic interpretability strategies such as probes, interventions or staring at all weights.
| Comments: | Codes are available here:this https URL |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Representation Theory (math.RT); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2305.08746 [cs.NE] |
| (orarXiv:2305.08746v3 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2305.08746 arXiv-issued DOI via DataCite |
Submission history
From: Ziming Liu [view email][v1] Thu, 4 May 2023 17:56:42 UTC (24,834 KB)
[v2] Fri, 19 May 2023 20:19:26 UTC (24,834 KB)
[v3] Tue, 6 Jun 2023 16:11:42 UTC (24,835 KB)
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View a PDF of the paper titled Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability, by Ziming Liu and 2 other authors
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