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Found 6 papers, 4 papers with code
Learning multi-modal generative models with permutation-invariant encoders and tighter variational bounds
In order to encode latent variables from different modality subsets, Product-of-Experts (PoE) or Mixture-of-Experts (MoE) aggregation schemes have been routinely used and shown to yield different trade-offs, for instance, regarding their generative quality or consistency across multiple modalities.
Learning variational autoencoders via MCMC speed measures
Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximizing an Evidence Lower Bound (ELBO).
Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods.
Entropy-based adaptive Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution.
Copula-like Variational Inference
This family is based on new copula-like densities on the hypercube with non-uniform marginals which can be sampled efficiently, i. e. with a complexity linear in the dimension of state space.
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
We present a scalable approach to performing approximate fully Bayesian inference in generic state space models.
