Identifiability of deep generative models without auxiliary information
We prove identifiability of a broad class of deep latent variable models that (a) have
universal approximation capabilities and (b) are the decoders of variational autoencoders …
universal approximation capabilities and (b) are the decoders of variational autoencoders …
Approximation by finite mixtures of continuous density functions that vanish at infinity
Given sufficiently many components, it is often cited that finite mixture models can
approximate any other probability density function (pdf) to an arbitrary degree of accuracy …
approximate any other probability density function (pdf) to an arbitrary degree of accuracy …
Bayesian infinite mixture models for wind speed distribution estimation
Wind energy, as a clean, environment-friendly, and inexhaustible renewable energy, has
attracted significant attention, and wind speed distribution plays an important role in its …
attracted significant attention, and wind speed distribution plays an important role in its …
[图书][B] Compendium of Neurosymbolic Artificial Intelligence
If only it were possible to develop automated and trainable neural systems that could justify
their behavior in a way that could be interpreted by humans like a symbolic system. The field …
their behavior in a way that could be interpreted by humans like a symbolic system. The field …
Stochastic loss reserving with mixture density neural networks
In recent years, new techniques based on artificial intelligence and machine learning in
particular have been making a revolution in the work of actuaries, including in loss …
particular have been making a revolution in the work of actuaries, including in loss …
Probabilistic dose prediction using mixture density networks for automated radiation therapy treatment planning
We demonstrate the application of mixture density networks (MDNs) in the context of
automated radiation therapy treatment planning. It is shown that an MDN can produce good …
automated radiation therapy treatment planning. It is shown that an MDN can produce good …
Dual mixture model based cnn for image denoising
Z Li, F Wang, L Cui, J Liu - IEEE Transactions on Image …, 2022 - ieeexplore.ieee.org
Non-Gaussian residual error and noise are common in the real applications, and they can
be efficiently addressed by some non-quadratic fidelity terms in the classic variational …
be efficiently addressed by some non-quadratic fidelity terms in the classic variational …
Testing for the Markov property in time series via deep conditional generative learning
The Markov property is widely imposed in analysis of time series data. Correspondingly,
testing the Markov property, and relatedly, inferring the order of a Markov model, are of …
testing the Markov property, and relatedly, inferring the order of a Markov model, are of …
Uniform consistency in nonparametric mixture models
B Aragam, R Yang - The Annals of Statistics, 2023 - projecteuclid.org
Uniform consistency in nonparametric mixture models Page 1 The Annals of Statistics 2023,
Vol. 51, No. 1, 362–390 https://doi.org/10.1214/22-AOS2255 © Institute of Mathematical …
Vol. 51, No. 1, 362–390 https://doi.org/10.1214/22-AOS2255 © Institute of Mathematical …
Regression‐based heterogeneity analysis to identify overlapping subgroup structure in high‐dimensional data
Z Luo, X Yao, Y Sun, X Fan - Biometrical Journal, 2022 - Wiley Online Library
Heterogeneity is a hallmark of complex diseases. Regression‐based heterogeneity
analysis, which is directly concerned with outcome–feature relationships, has led to a …
analysis, which is directly concerned with outcome–feature relationships, has led to a …