Concept
Variational Bayesian methods
Related publications (121)
The complexity of quantum support vector machines
Gian Florin Gentinetta, Stefan Woerner
Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models c ...
Wien2024Robust Outlier Rejection for 3D Registration with Variational Bayes
Mathieu Salzmann, Jiancheng Yang, Zheng Dang, Zhen Wei, Haobo Jiang
Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...
Los Alamitos2023From Probability Graphical Models to Dynamic Networks — A Bayesian perspective on Smooth Best Estimate of Trajectory with applications in Geodetic Engineering
Laurent Valentin Jospin, Jesse Ray Murray Lahaye
Bayesian statistics is concerned with the integration of new information obtained through observations with prior knowledge, and accordingly, is often related to information theory (Jospin 2022). Recursive Bayesian estimation methods, such as Kalman Filter ...
2023Preliminary design of the cold mass supports for the EU DEMO feeders
The Cold Mass Support (CMS) is a basic structural component of the feeder system of the EU DEMO tokamak. The function of this structural element is not only to provide support to the containment duct - wiring the cryogenic lines, electrical bus bars and in ...
ELSEVIER SCIENCE SA2023Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France
Elevated nitrate from human activity causes ecosystem and economic harm globally. The factors that control the spatiotemporal dynamics of riverine nitrate concentration remain difficult to describe and predict. We analyzed nitrate concentration from 4450 s ...
SPRINGER2022Information-driven transitions in projections of underdamped dynamics
Daniel Maria Busiello, Giorgio Nicoletti, Amos Maritan
Low-dimensional representations of underdamped systems often provide useful insights and analytical tractability. Here, we build such representations via information projections, obtaining an optimal model that captures the most information on observed spa ...
AMER PHYSICAL SOC2022Contextually Plausible and Diverse 3D Human Motion Prediction
Mathieu Salzmann, Fatemehsadat Saleh
We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). How ...
IEEE2021Fast Bayesian estimation of spatial count data models
Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Mont ...
2021Learning in Volatile Environments With the Bayes Factor Surprise
Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni
Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...
MIT PRESS2021Learning Self-Exciting Temporal Point Processes Under Noisy Observations
Understanding the diffusion patterns of sequences of interdependent events is a central question for a variety of disciplines. Temporal point processes are a class of elegant and powerful models of such sequences; these processes have become popular across ...
EPFL2021