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Found 9 papers, 1 papers with code
Deep Learning Enhanced Dynamic Mode Decomposition
Koopman operator theory shows how nonlinear dynamical systems can be represented as an infinite-dimensional, linear operator acting on a Hilbert space of observables of the system.
Lifted Weighted Mini-Bucket
Many graphical models, such as Markov Logic Networks (MLNs) with evidence, possess highly symmetric substructures but no exact symmetries.
Dynamic Importance Sampling for Anytime Bounds of the Partition Function
Our algorithm combines and generalizes recent work on anytime search and probabilistic bounds of the partition function.
Probabilistic Variational Bounds for Graphical Models
We propose a simple Monte Carlo based inference method that augments convex variational bounds by adding importance sampling (IS).
Variational Planning for Graph-based MDPs
The KL divergence is optimized using the belief propagation algorithm, with complexity exponential in only the cluster size of the graph.
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
We study the problem of estimating continuous quantities, such as prices, probabilities, and point spreads, using a crowdsourcing approach.
Variational Inference for Crowdsourcing
Crowdsourcing has become a popular paradigm for labeling large datasets.
Particle-based Variational Inference for Continuous Systems
Since the development of loopy belief propagation, there has been considerable work on advancing the state of the art for approximate inference over distributions defined on discrete random variables.
A Low Density Lattice Decoder via Non-Parametric Belief Propagation
We show that the LDLC decoder is an instance of non-parametric belief propagation and further connect it to the Gaussian belief propagation algorithm.
Information Theory Information Theory
