586 research outputs found
Hyperbolic Deep Neural Networks: A Survey
Recently, there has been a rising surge of momentum for deep representation
learning in hyperbolic spaces due to theirhigh capacity of modeling data like
knowledge graphs or synonym hierarchies, possessing hierarchical structure. We
refer to the model as hyperbolic deep neural network in this paper. Such a
hyperbolic neural architecture potentially leads to drastically compact model
withmuch more physical interpretability than its counterpart in Euclidean
space. To stimulate future research, this paper presents acoherent and
comprehensive review of the literature around the neural components in the
construction of hyperbolic deep neuralnetworks, as well as the generalization
of the leading deep approaches to the Hyperbolic space. It also presents
current applicationsaround various machine learning tasks on several publicly
available datasets, together with insightful observations and identifying
openquestions and promising future directions
Masked Autoregressive Flow for Density Estimation
Autoregressive models are among the best performing neural density
estimators. We describe an approach for increasing the flexibility of an
autoregressive model, based on modelling the random numbers that the model uses
internally when generating data. By constructing a stack of autoregressive
models, each modelling the random numbers of the next model in the stack, we
obtain a type of normalizing flow suitable for density estimation, which we
call Masked Autoregressive Flow. This type of flow is closely related to
Inverse Autoregressive Flow and is a generalization of Real NVP. Masked
Autoregressive Flow achieves state-of-the-art performance in a range of
general-purpose density estimation tasks.Comment: section 4.3 is corrected since the previous versio
- …