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Paul Szerlip

Paul Szerlip
Paul Szerlip earned his PhD with Dr. Kenneth Stanley at the University of Central Florida focusing on open-source infrastructure for collaborative evolutionary software. This open-source platform enables researchers to quickly integrate crowd-sourced human contributions with automated algorithms, while making the results easily accessible online. His later research highlighted new ways to integrate neuroevolutionary techniques like HyperNEAT and Novelty Search into deep learning frameworks.

Research Papers

Pyro: Deep Universal Probabilistic Programming

E. Bingham, J. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, N. Goodman
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. [...] [PDF]
Journal of Machine Learning Research (JMLR), 2018

Characterizing how Visual Question Answering models scale with the world

E. Bingham, P. Molino, P. Szerlip, F. Obermeyer, N. Goodman
Detecting differences in generalization ability between models for visual question answering tasks has proven to be surprisingly difficult. We propose a new statistic, asymptotic sample complexity, for model comparison, and construct a synthetic data distribution to compare a strong baseline CNN-LSTM model to a structured neural network with powerful inductive biases. [...] [PDF]
ViGIL @ NeurIPS(NeurIPS), 2017

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