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Eli Bingham

Eli Bingham
Eli is a research scientist working on probabilistic programming, approximate Bayesian inference, and grounded language understanding. He has previously worked on condensed matter physics, computational biology, climatology, multiscale dictionary learning, and deep learning for computer vision. In his spare time he hangs out in a lab tinkering with his nanopore DNA sequencer.

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 at arXiv]
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 at Github]
ViGIL @ NeurIPS(NeurIPS), 2017

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