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Found 4 papers, 2 papers with code
Common Decomposition of Correlated Brownian Motions and its Financial Applications
In this paper, we develop a theory of common decomposition for two correlated Brownian motions, in which, by using change of time method, the correlated Brownian motions are represented by a triplet of processes, $(X, Y, T)$, where $X$ and $Y$ are independent Brownian motions.
Deep Music Analogy Via Latent Representation Disentanglement
Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces.
Inspecting and Interacting with Meaningful Music Representations using VAE
Variational Autoencoders(VAEs) have already achieved great results on image generation and recently made promising progress on music generation.
Efficient Architecture Search by Network Transformation
Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results.
Ranked #140 on
Image Classification
on CIFAR-10
