A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Structured Stochastic Variational Inference
[article]
2014
arXiv
pre-print
Preserved Fulltext
Other Versions
Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization. ...
The algorithm relies on the use of fully factorized variational distributions. ...
Structured Stochastic Variational Inference In this section, we will present two SSVI algorithms. ...
arXiv:1404.4114v3
fatcat:hsineim6lbcmriqce4wbexshfq
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
[article]
2014
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We leverage the recently developed stochastic structured mean-field variational inference to relax the conjugacy constraint and restore the dependencies among the latent variables in the approximating ...
In this paper, we derive a structured mean-field variational inference algorithm for a beta process non-negative matrix factorization (NMF) model with Poisson likelihood. ...
Stochastic structured mean-field variational inference Following the stochastic structure mean-field variational inference framework, we divide the latent random variables into local: {Z t , s t } T t= ...
arXiv:1411.1804v2
fatcat:44fhuvvrn5hyhkscf2qfogzute
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
This paper presents an efficient variational inference framework for deriving a family of structured gaussian process regression network (SGPRN) models. ...
Then we propose structured variable distributions and marginalize latent variables, which enables the decomposability of a tractable variational lower bound and leads to stochastic optimization. ...
stochastic variational inference. ...
arXiv:2106.00719v2
fatcat:xjzxarth3bdsvac6vvlarsl4na
Automated Variational Inference in Probabilistic Programming
[article]
2013
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. ...
This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly structured distributions ...
Finally, stochastic gradient methods are also used in online variational inference algorithms, in particular in the work of Blei et al. in stochastic variational inference (for instance, online LDA [19 ...
arXiv:1301.1299v1
fatcat:jchutrnmkfeqbob27lk5oz3hy4
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We propose a scalable inference and learning algorithm for FHMMs that draws on ideas from the stochastic variational inference, neural network and copula literatures. ...
Our experiments corroborate that the proposed algorithm does not introduce further approximation bias compared to the proven structured mean-field algorithm, and achieves better performance with long sequences ...
The Message Free Stochastic Variational Inference Algorithm While structured mean-field variational inference and its associated EM algorithms are effective tools for inference and learning in FHMMs with ...
arXiv:1608.03817v3
fatcat:fzss76cwwzhanff45khahy3kq4
Stochastic Variational Inference
[article]
2013
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Stochastic variational inference lets us apply complex Bayesian models to massive data sets. ...
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. ...
In this appendix, we show how to do stochastic variational inference under the weaker assumption that we can break the global parameter vector β into a set of K subvectors β 1:K such that each conditional ...
arXiv:1206.7051v3
fatcat:hsdtdfvs6ne2dnhefqokmtfua4
Variational inference for neural network matrix factorization and its application to stochastic blockmodeling
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We describe a variational inference algorithm for a neural network matrix factorization model with nonparametric block structure and evaluate its performance on the NIPS co-authorship data set. ...
Such a probabilistic approach is required, however, when considering the important class of stochastic block models. ...
Stochastic variational inference We consider letting f θ be a Bayesian neural network and elect a mean-field variational approach to inference. ...
arXiv:1905.04502v3
fatcat:sihppqsaubgd5j62prty5vun7m
Graph Representation Learning via Ladder Gamma Variational Autoencoders
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We present a probabilistic framework for community discovery and link prediction for graph-structured data, based on a novel, gamma ladder variational autoencoder (VAE) architecture. ...
In addition to leveraging the representational power of multiple layers of stochastic variables via the ladder VAE architecture, our framework offers the following benefits: (1) Unlike existing ladder ...
Thus we use stochastic gradient variational Bayes (SGVB) (Kingma and Welling 2013; 2014) to perform inference for our model. ...
doi:10.1609/aaai.v34i04.6013
fatcat:be2y5orhzbhntd5oeckulc5wtm
Stochastic Blockmodels meet Graph Neural Networks
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
They have proven to be successful for various tasks, such as discovering the community structure and link prediction on graph-structured data. ...
Moreover, our framework is accompanied by a fast recognition model that enables fast inference of the node embeddings (which are of independent interest for inference in SBM and its variants). ...
Structured Mean-Field: Since the vanilla mean-field variational inference ignores the posterior dependence among the latent variables, we also considered Structured Stochastic Variational Inference (SSVI ...
arXiv:1905.05738v1
fatcat:udjb3hyrx5d3bms5kuhan3zkny
Copula variational inference
[article]
2015
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
With stochastic optimization, inference on the augmented distribution is scalable. ...
We develop a general variational inference method that preserves dependency among the latent variables. ...
Combined with stochastic optimization, variational inference can scale complex statistical models to massive data sets [9, 23, 24] . ...
arXiv:1506.03159v2
fatcat:escqrubahvas5h4asobl2jaypq
Truncation-free Online Variational Inference for Bayesian Nonparametric Models
2012
Neural Information Processing Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Our method performs better than previous stochastic variational inference algorithms. ...
We present a truncation-free stochastic variational inference algorithm for Bayesian nonparametric models. ...
Thus we need to truncate the variational distribution [13, 14] . Truncation is necessary in variational inference because of the mathematical structure of BNP models. ...
dblp:conf/nips/WangB12
fatcat:o5z74ffkwngwdb7xbjaggc6gni
Variational Training for Large-Scale Noisy-OR Bayesian Networks
2019
Conference on Uncertainty in Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We propose a stochastic variational inference algorithm for training large-scale Bayesian networks, where noisy-OR conditional distributions are used to capture higher-order relationships. ...
Using stochastic gradient updates based on our variational bounds, we learn noisy-OR Bayesian networks orders of magnitude faster than was possible with prior Monte Carlo learning algorithms, and provide ...
Michael Hughes and Jialu Liu provided helpful information about baseline topic models and inference algorithms. ...
dblp:conf/uai/0001CNYZXS19
fatcat:ibqkwflconhsfjjx3pxi67266m
Stochastic Bayesian Neural Networks
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2008.07587v2 fatcat:6eoesgr2a5byzoch5dlxjmyj74Other Versions
Our work builds on variational inference techniques for bayesian neural networks using the original Evidence Lower Bound. ...
Bayesian neural networks perform variational inference over the weights however calculation of the posterior distribution remains a challenge. ...
We proposed a new lower bound using variational inference techniques which we named as Stochastic Evidence Lower Bound. ...
arXiv:2008.07587v3
fatcat:qtsfg65rg5eqle2sga2jktg3ye
Ladder Variational Autoencoders
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Finally, we observe that batch normalization and deterministic warm-up (gradually turning on the KL-term) are crucial for training variational models with many stochastic layers. ...
We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently ...
We propose a new structured inference model using the same top-down dependency structure both in the inference and generative models. ...
arXiv:1602.02282v3
fatcat:53z5qnfysbfcpmo6stklz7vwfy
Deep Probabilistic Programming
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. ...
In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. ...
As an example, Appendix B shows how to implement stochastic variational inference in Edward. ...
arXiv:1701.03757v2
fatcat:f3zxlird3bbpblw2fcrq3zuypm
« Previous
Showing results 1 — 15 out of 170,184 results