Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study. Designing flexible and scalable generative models is a very challenging task due to the high dimensionality of the data, as well as the need to represent temporal dependencies and marginal network structure. Here we introduce DAMNETS, a scalable deep generative model for network time series. DAMNETS outperforms competing methods on all of our measures of sample quality, over both real and synthetic data sets
International audienceThis paper brings deep learning at the forefront of research into Time Series ...
We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic ne...
Dynamic networks models describe a growing number of important scientific processes, from cell biolo...
In this work, we introduce DAMNETS, a deep generative model for Markovian network time series. Time ...
We model a large panel of time series as a vector autoregression where the autoregressive matrices a...
124 pagesModern datasets for machine learning and AI applications leverage vast data across multiple...
In this work I explore deep learning from the basics to more complex theories like recurrent network...
Many real-world tasks are plagued by limitations on data: in some instances very little data is avai...
This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new cl...
Nowadays, time series are a widely-exploited methodology to describe phenomena belonging to differen...
This paper introduces a new class of Bayesian dynamic models for inference and forecasting in high-d...
We model a large panel of time series as a var where the autoregressive matrices and the inverse cov...
Multivariate time series generation is a promising method for sharing sensitive data in numerous med...
Time series classification (TSC) is widely used in various real-world applications such as human act...
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenome...
International audienceThis paper brings deep learning at the forefront of research into Time Series ...
We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic ne...
Dynamic networks models describe a growing number of important scientific processes, from cell biolo...
In this work, we introduce DAMNETS, a deep generative model for Markovian network time series. Time ...
We model a large panel of time series as a vector autoregression where the autoregressive matrices a...
124 pagesModern datasets for machine learning and AI applications leverage vast data across multiple...
In this work I explore deep learning from the basics to more complex theories like recurrent network...
Many real-world tasks are plagued by limitations on data: in some instances very little data is avai...
This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new cl...
Nowadays, time series are a widely-exploited methodology to describe phenomena belonging to differen...
This paper introduces a new class of Bayesian dynamic models for inference and forecasting in high-d...
We model a large panel of time series as a var where the autoregressive matrices and the inverse cov...
Multivariate time series generation is a promising method for sharing sensitive data in numerous med...
Time series classification (TSC) is widely used in various real-world applications such as human act...
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenome...
International audienceThis paper brings deep learning at the forefront of research into Time Series ...
We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic ne...
Dynamic networks models describe a growing number of important scientific processes, from cell biolo...
Add to ORKG