Add to ORKGThis paper applies a generative deep learning model, namely a Variational Autoencoder, on probabilistic optimal power flows. The model utilizes Gaussian approximations in order to adequately represent the distributions of the results of a system under uncertainty. These approximations are realized by applying several techniques from Bayesian deep learning, among them most notably Stochastic Variational Inference. Using the reparameterization trick and batch sampling, the proposed model allows for the training a probabilistic optimal power flow similar to a possibilistic process. The results are shown by application of a reformulation of the Kullback-Leibler divergence, a distance measure of distributions. Not only is the resulting model sim...
Many modern machine learning methods, including deep neural networks, utilize a discrete sequence of...
The load flow problem is fundamental to characterize the equilibrium behavior of a power system. Unc...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised ...
This paper applies a generative deep learning model, namely a Variational Autoencoder, on probabilis...
In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow ...
Probabilistic inference is at the core of many recent advances in machine learning. Unfortunately, ...
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic mo...
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm ...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
We propose a simple method that combines neural networks and Gaussian processes. The proposed method...
The automation of probabilistic reasoning is one of the primary aims of machine learning. Recently, ...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
High dimensional probabilistic models are used for many modern scientific and engineering data analy...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a gen-eralised...
© ICLR 2016: San Juan, Puerto Rico. All Rights Reserved. We develop a scalable deep non-parametric g...
Many modern machine learning methods, including deep neural networks, utilize a discrete sequence of...
The load flow problem is fundamental to characterize the equilibrium behavior of a power system. Unc...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised ...
This paper applies a generative deep learning model, namely a Variational Autoencoder, on probabilis...
In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow ...
Probabilistic inference is at the core of many recent advances in machine learning. Unfortunately, ...
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic mo...
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm ...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
We propose a simple method that combines neural networks and Gaussian processes. The proposed method...
The automation of probabilistic reasoning is one of the primary aims of machine learning. Recently, ...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
High dimensional probabilistic models are used for many modern scientific and engineering data analy...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a gen-eralised...
© ICLR 2016: San Juan, Puerto Rico. All Rights Reserved. We develop a scalable deep non-parametric g...
Many modern machine learning methods, including deep neural networks, utilize a discrete sequence of...
The load flow problem is fundamental to characterize the equilibrium behavior of a power system. Unc...
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised ...