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Found 7 papers, 3 papers with code
Rare Event Probability Learning by Normalizing Flows
Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal.
KirchhoffNet: A Circuit Bridging Message Passing and Continuous-Depth Models
Moreover, we justify that irrespective of the number of parameters within a KirchhoffNet, its forward calculation can always be completed within 1/f seconds, with f representing the hardware's clock frequency.
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
In this work, for the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation.
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Data lies at the core of modern deep learning.
FreDo: Frequency Domain-based Long-Term Time Series Forecasting
Finally, we validate that the frequency domain is indeed better by comparing univariate models trained in the frequency v. s.
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
A common assumption in training neural networks via maximum likelihood estimation on time series is that the errors across time steps are uncorrelated.
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network
Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model.
