240 citations · 316 across the 5 of their papers we have counts for
6 papers
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Christopher Aicher, Nicholas J. Foti, Emily B. Fox
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 truncat…
Stochastic Gradient MCMC for Nonlinear State Space Models
Christopher Aicher, Srshti Putcha, Christopher Nemeth +2
State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled…
Stochastic Gradient MCMC for State Space Models
Christopher Aicher, Yi-An Ma, Nicholas J. Foti +1
State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic…
Approximate Collapsed Gibbs Clustering with Expectation Propagation
Christopher Aicher, Emily B. Fox
We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models. Collapsed Gibbs is a popular MCMC method, which integrates out varia…
Learning Latent Block Structure in Weighted Networks
Christopher Aicher, Abigail Z. Jacobs, Aaron Clauset
Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity p…
Adapting the Stochastic Block Model to Edge-Weighted Networks
Christopher Aicher, Abigail Z. Jacobs, Aaron Clauset
We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution. This generalization introduc…