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Found 6 papers, 3 papers with code
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncating backpropagation after a fixed number of lags.
Stochastic Gradient MCMC for Nonlinear State Space Models
We evaluate our proposed particle buffered stochastic gradient using stochastic gradient MCMC for inference on both long sequential synthetic and minute-resolution financial returns data, demonstrating the importance of this class of methods.
Stochastic Gradient MCMC for State Space Models
However, inference in SSMs is often computationally prohibitive for long time series.
Approximate Collapsed Gibbs Clustering with Expectation Propagation
We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models.
Learning Latent Block Structure in Weighted Networks
We then evaluate the WSBM's performance on both edge-existence and edge-weight prediction tasks for a set of real-world weighted networks.
Adapting the Stochastic Block Model to Edge-Weighted Networks
We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution.
