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Found 6 papers, 4 papers with code
Framing RNN as a kernel method: A neural ODE approach
Building on the interpretation of a recurrent neural network (RNN) as a continuous-time neural differential equation, we show, under appropriate conditions, that the solution of a RNN can be viewed as a linear function of a specific feature set of the input sequence, known as the signature.
Implicit regularization of deep residual networks towards neural ODEs
Our results are valid for a finite training time, and also as the training time tends to infinity provided that the network satisfies a Polyak-Lojasiewicz condition.
Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs
On CSQA, our approach increases the coverage from $80\%$ to $96. 2\%$, and the LF execution accuracy from $70. 6\%$ to $75. 6\%$, with respect to previous state-of-the-art results.
Scaling ResNets in the Large-depth Regime
initializations, the only non-trivial dynamics is for $\alpha_L = 1/\sqrt{L}$ (other choices lead either to explosion or to identity mapping).
Leveraging the two timescale regime to demonstrate convergence of neural networks
We study the training dynamics of shallow neural networks, in a two-timescale regime in which the stepsizes for the inner layer are much smaller than those for the outer layer.
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions.
