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Found 4 papers, 4 papers with code
Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length
Neural networks offer good approximation to many tasks but consistently fail to reach perfect generalization, even when theoretical work shows that such perfect solutions can be expressed by certain architectures.
Minimum Description Length Hopfield Networks
Associative memory architectures are designed for memorization but also offer, through their retrieval method, a form of generalization to unseen inputs: stored memories can be seen as prototypes from this point of view.
Benchmarking Neural Network Generalization for Grammar Induction
How well do neural networks generalize?
Minimum Description Length Recurrent Neural Networks
We train neural networks to optimize a Minimum Description Length score, i. e., to balance between the complexity of the network and its accuracy at a task.
