101 citations · 133 across the 7 of their papers we have counts for
7 papers
ELBOing Stein: Variational Bayes with Stein Mixture Inference
Ola Rønning, Eric Nalisnick, Christophe Ley +2
Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles. However, SVGD suffers…
Dropout as a Structured Shrinkage Prior
Eric Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth
Dropout regularization of deep neural networks has been a mysterious yet effective tool to prevent overfitting. Explanations for its success range from the prevention of "co-adapte…
Mondrian Processes for Flow Cytometry Analysis
Disi Ji, Eric Nalisnick, Padhraic Smyth
Analysis of flow cytometry data is an essential tool for clinical diagnosis of hematological and immunological conditions. Current clinical workflows rely on a manual process calle…
Learning Approximately Objective Priors
Eric Nalisnick, Padhraic Smyth
Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective priors. However, objective priors such as…
A Dual Embedding Space Model for Document Ranking
Bhaskar Mitra, Eric Nalisnick, Nick Craswell +1
A fundamental goal of search engines is to identify, given a query, documents that have relevant text. This is intrinsically difficult because the query and the document may use di…
Learning the Dimensionality of Word Embeddings
Eric Nalisnick, Sachin Ravi
We describe a method for learning word embeddings with data-dependent dimensionality. Our Stochastic Dimensionality Skip-Gram (SD-SG) and Stochastic Dimensionality Continuous Bag-o…
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Eric Nalisnick, Anima Anandkumar, Padhraic Smyth
Corrupting the input and hidden layers of deep neural networks (DNNs) with multiplicative noise, often drawn from the Bernoulli distribution (or 'dropout'), provides regularization…