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Found 4 papers, 4 papers with code
Meta-learning autoencoders for few-shot prediction
Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges.
Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles.
Computational Physics Applied Physics Optics
Gated Orthogonal Recurrent Units: On Learning to Forget
We present a novel recurrent neural network (RNN) based model that combines the remembering ability of unitary RNNs with the ability of gated RNNs to effectively forget redundant/irrelevant information in its memory.
Ranked #7 on
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(Accuracy (trained on 1k) metric)
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn long-term correlations in the data.
