Add to ORKGIn this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization
This paper develops variational continual learning (VCL), a simple but general framework for continu...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
Amortized variational inference, whereby the inferred latent variable posterior distributions are pa...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
The ever-increasing size of modern data sets combined with the difficulty of obtaining label informa...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a gen-eralised...
Variational inference (VI) or Variational Bayes (VB) is a popular alternative to MCMC, which doesn\u...
© ICLR 2016: San Juan, Puerto Rico. All Rights Reserved. We develop a scalable deep non-parametric g...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Deep generative models allow us to learn hidden representations of data and generate new examples. T...
Advances in deep generative models are at the forefront of deep learning research because of the pro...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised ...
Variational inference and learning for a unified model of syntax, semantics and morpholog
This paper develops variational continual learning (VCL), a simple but general framework for continu...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
Amortized variational inference, whereby the inferred latent variable posterior distributions are pa...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
The ever-increasing size of modern data sets combined with the difficulty of obtaining label informa...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a gen-eralised...
Variational inference (VI) or Variational Bayes (VB) is a popular alternative to MCMC, which doesn\u...
© ICLR 2016: San Juan, Puerto Rico. All Rights Reserved. We develop a scalable deep non-parametric g...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Deep generative models allow us to learn hidden representations of data and generate new examples. T...
Advances in deep generative models are at the forefront of deep learning research because of the pro...
Bayesian statistics is a powerful framework for modeling the world and reasoning over uncertainty. I...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised ...
Variational inference and learning for a unified model of syntax, semantics and morpholog
This paper develops variational continual learning (VCL), a simple but general framework for continu...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
Amortized variational inference, whereby the inferred latent variable posterior distributions are pa...