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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
TLDR
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.
Black Box Variational Inference
TLDR
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.
Automatic Differentiation Variational Inference
TLDR
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.
Variational Sequential Monte Carlo
TLDR
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.
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
TLDR
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.
Hierarchical Variational Models
TLDR
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.
Unsupervised learning of hierarchical representations with convolutional deep belief networks
TLDR
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.
Automatic Variational Inference in Stan
TLDR
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.
Operator Variational Inference
TLDR
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.
Variational Inference via χ Upper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. It posits a family of approximating distributions q and finds the closest member to the exact
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