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Symbolic Autoencoding for Self-Supervised Sequence Learning
Traditional language models, adept at next-token prediction in text sequences, often struggle with transduction tasks between distinct symbolic systems, particularly when parallel data is scarce.
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization
The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks.
Sharp asymptotics on the compression of two-layer neural networks
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes.
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