495 citations · 2.3k across the 36 of their papers we have counts for
8 papers · 1 filter
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Akash Srivastava, Lazar Valkov, Chris Russell +2
Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images. But many of these methods, including generative adversa…
Autoencoding Variational Inference For Topic Models
Akash Srivastava, Charles Sutton
Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving…
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
Akash Srivastava, James Zou, Ryan P. Adams +1
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criter…
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
Akash Srivastava, James Zou, Charles Sutton
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criter…
A Subsequence Interleaving Model for Sequential Pattern Mining
Jaroslav Fowkes, Charles Sutton
Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most comp…
Latent Bayesian melding for integrating individual and population models
Mingjun Zhong, Nigel Goddard, Charles Sutton
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of ind…