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Linear RNNs Provably Learn Linear Dynamic Systems
We study the learning ability of linear recurrent neural networks with Gradient Descent.
On the Provable Generalization of Recurrent Neural Networks
In this paper, using detailed analysis about the neural tangent kernel matrix, we prove a generalization error bound to learn such functions without normalized conditions and show that some notable concept classes are learnable with the numbers of iterations and samples scaling almost-polynomially in the input length $L$.
Coarse-grained decomposition and fine-grained interaction for multi-hop question answering
Recent advances regarding question answering and reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text, requiring only single-hop reasoning.
