Summary of Coding Schemes in Neural Networks Learning Classification Tasks, by Alexander Van Meegen et al.
Coding schemes in neural networks learning classification tasks
by Alexander van Meegen, Haim Sompolinsky
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (stat.ML)
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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 In this paper, researchers investigate the internal representations generated by neural networks when learning classification tasks using a Bayesian framework. They focus on fully-connected, wide neural networks and find that the nature of these representations depends crucially on the neuronal nonlinearity. In linear networks, an analog coding scheme emerges, while in nonlinear networks, spontaneous symmetry breaking leads to either redundant or sparse coding schemes. The study highlights how network properties like scaling of weights and neuronal nonlinearity can profoundly influence the emergent representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how neural networks learn things. It uses a special way of looking at the networks called Bayesian framework. The researchers looked at big, complicated networks and found out that what they’re learning depends on whether the network is linear or not. If it’s linear, it learns one kind of thing, but if it’s non-linear, it learns another kind of thing. This matters because it helps us understand how our brains work and how we can make computers learn like humans. |
Keywords
* Artificial intelligence * Classification
