680 citations · 812 across the 12 of their papers we have counts for
13 papers
DynaConF: Dynamic Forecasting of Non-Stationary Time Series
Siqi Liu, Andreas Lehrmann
Deep learning has shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence. H…
Continuous-time Particle Filtering for Latent Stochastic Differential Equations
Ruizhi Deng, Greg Mori, Andreas M. Lehrmann
Particle filtering is a standard Monte-Carlo approach for a wide range of sequential inference tasks. The key component of a particle filter is a set of particles with importance w…
Efficient CDF Approximations for Normalizing Flows
Chandramouli Shama Sastry, Andreas Lehrmann, Marcus Brubaker +1
Normalizing flows model a complex target distribution in terms of a bijective transform operating on a simple base distribution. As such, they enable tractable computation of a num…
Continuous Latent Process Flows
Ruizhi Deng, Marcus A. Brubaker, Greg Mori +1
Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines. Fitting this type of data using statistical models with continuous dynam…
Unsupervised Video Decomposition using Spatio-temporal Iterative Inference
Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal +1
Unsupervised multi-object scene decomposition is a fast-emerging problem in representation learning. Despite significant progress in static scenes, such models are unable to levera…
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Ruizhi Deng, Bo Chang, Marcus A. Brubaker +2
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this…