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"Flexible and Robust Particle Tempering for State Space Models" by David Gunawan, Robert Kohn et al.

Density tempering (also called density annealing) is a sequential Monte Carlo approach to Bayesian inference for general state models which is an alternative to Markov chain Monte Carlo. When applied to state space models, it moves a collection of parameters and latent states (which are called particles) through a number of stages, with each stage having its own target distribution. The particles are initially generated from a distribution that is easy to sample from, e.g. the prior; the target ...
Monte Carlo Diffusion Process Actor Stochastic Volatility Model Hamiltonian Monte Carlo Article Markov Chain Monte Carlo Structural Change
Source: uow.edu.au

"Diffusion Kernel Attention Network for Brain Disorder Classification" by Jianjia Zhang, Luping Zhou et al.

Constructing and analyzing functional brain networks (FBN) has become a promising approach to brain disorder classification. However, the conventional successive construct-and-analyze process would limit the performance due to the lack of interactions and adaptivity among the subtasks in the process. Recently, Transformer has demonstrated remarkable performance in various tasks, attributing to its effective attention mechanism in modeling complex feature relationships. In this paper, for the fir...
Diffusion Kernel Attention Network Attention Network Rain Disease Classification Brain Modeling Drain Network Diffusion Process
Source: uow.edu.au

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