From Latent Space to Training Data: Explainable Specialization in Minimal MLPs
📰 ArXiv cs.AI
Learn how to improve prototype-based reconstruction of training data in minimal MLPs through explainable specialization of hidden neurons
Action Steps
- Build a minimal one-hidden-layer MLP with Gaussian activation
- Apply structural losses to encourage coverage of training samples
- Configure the model to promote separation between neuron-induced prototypes
- Test the model's ability to reconstruct the training dataset from learned weights
- Compare the performance of different structural losses on the reconstruction task
Who Needs to Know This
ML researchers and engineers working on neural network interpretability and explainability can benefit from this research to improve their models' performance and understanding
Key Insight
💡 Explainable specialization of hidden neurons in minimal MLPs can improve prototype-based reconstruction of training data
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Improve neural network interpretability with explainable specialization in minimal MLPs! #ML #Explainability
Key Takeaways
Learn how to improve prototype-based reconstruction of training data in minimal MLPs through explainable specialization of hidden neurons
Full Article
Title: From Latent Space to Training Data: Explainable Specialization in Minimal MLPs
Abstract:
arXiv:2605.25939v1 Announce Type: cross Abstract: We here study whether training biases can make hidden neurons specialize in minimal one-hidden-layer MLPs, and whether such specialization improves prototype-based reconstruction of the training dataset from the learned weights. We consider Gaussianactivation MLPs of width equal to dataset size and compare three structural losses that respectively encourage coverage of the training samples, separation between neuron-induced prototypes, and low ov
Abstract:
arXiv:2605.25939v1 Announce Type: cross Abstract: We here study whether training biases can make hidden neurons specialize in minimal one-hidden-layer MLPs, and whether such specialization improves prototype-based reconstruction of the training dataset from the learned weights. We consider Gaussianactivation MLPs of width equal to dataset size and compare three structural losses that respectively encourage coverage of the training samples, separation between neuron-induced prototypes, and low ov
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