Author name cluster

Paul Szerlip

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

JMLR Journal 2019 Journal Article

Pyro: Deep Universal Probabilistic Programming

Eli Bingham
Jonathan P. Chen
Martin Jankowiak
Fritz Obermeyer
Neeraj Pradhan
Theofanis Karaletsos
Rohit Singh
Paul Szerlip

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2019. ( edit, beta )

PDF Details

AAAI Conference 2015 Conference Paper

Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

Paul Szerlip
Gregory Morse
Justin Pugh
Kenneth Stanley

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.

PDF Details