Skip to search formSkip to main contentSkip to account menu

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

The convolutional deep belief network is presented, a hierarchical generative model which scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference.
View on ACM (opens in a new tab)

Black Box Variational Inference

This paper presents a "black box" variational inference algorithm, one that can be quickly applied to many models with little additional derivation, based on a stochastic optimization of the variational objective where the noisy gradient is computed from Monte Carlo samples from the Variational distribution.
View PDF on arXiv (opens in a new tab)

Automatic Differentiation Variational Inference

Automatic differentiation variational inference (ADVI) is developed, where the scientist only provides a probabilistic model and a dataset, nothing else, and the algorithm automatically derives an efficient Variational inference algorithm, freeing the scientist to refine and explore many models.
View PDF on arXiv (opens in a new tab)

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans and outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit.
View PDF on arXiv (opens in a new tab)

Variational Sequential Monte Carlo

The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters, and is demonstrated its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.
View PDF on arXiv (opens in a new tab)

Hierarchical Variational Models

This work develops hierarchical variational models (HVMs), which augment a variational approximation with a prior on its parameters, which allows it to capture complex structure for both discrete and continuous latent variables.
View PDF on arXiv (opens in a new tab)

Unsupervised learning of hierarchical representations with convolutional deep belief networks

The convolutional deep belief network is presented, a hierarchical generative model that scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference.
View on ACM (opens in a new tab)

Automatic Variational Inference in Stan

An automatic variational inference algorithm, automatic differentiation Variational inference (ADVI), which is implemented in Stan, a probabilistic programming system and can be used on any model the authors write in Stan.
View PDF on arXiv (opens in a new tab)

Support and Invertibility in Domain-Invariant Representations

This work gives generalization bounds for unsupervised domain adaptation that hold for any representation function by acknowledging the cost of non-invertibility and proposes a bound based on measuring the extent to which the support of the source domain covers the target domain.
View PDF on arXiv (opens in a new tab)

Operator Variational Inference

A black box algorithm, operator variational inference (OPVI), for optimizing any operator objective, which can characterize different properties of variational objectives, such as objectives that admit data subsampling---allowing inference to scale to massive data---as well as objective that admit variational programs---a rich class of posterior approximations that does not require a tractable density.
View PDF on arXiv (opens in a new tab)
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)