Recently,there appear studies aiming atdesigning more efficient and practical algorithms for this problem.

We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.

Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision.

It communicates among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' collaborations are leveraged to perform multi-person tasks, a new challenge arisesduetoinformation asymmetry,sinceeachagentcanonlyaccess theinformationofitshumanuser.

Weextend our analysis to the more general class of vector symbolic architectures (VSA), which compute withhigh-dimensional vectors(hypervectors) thatarenotnecessarily binary.

This profoundly shapes the way we view and understand traditional MCMC sampling algorithms, deviating from the Markovsemigroup path.

Four Newton Lange /exp( ), taking (x)= log x21) l asthebarrier = 4 so Stepsizeish= 10 5. Projected constraints, directly byprojection andtheproximal solvedwith suggesting

First, most published methods rely on explicit knowledge of the construction oftheOODsplits.

While knowntobesample efficient, these methods havefailed tofully leverage recent advances indeep learning, forcing the use of less efficient but more scalable model-free methods which try to learn the values directly.