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Home Authors Posts by Noah Goodman

Noah Goodman

Noah Goodman
2 BLOG ARTICLES 3 RESEARCH PAPERS
In addition to working at Uber AI Labs, Noah is also an Associate Professor of Psychology, Computer Science, and Linguistics at Stanford University, where he runs the Computation and Cognition Lab.

Engineering Blog Articles

Pyro Accepted by the LF Deep Learning Foundation as a Hosted Project

Pyro, a deep, universal probabilistic programming language created by Uber AI, was voted in by the Linux Foundation Deep Learning Technical Board as the latest incubation project to join the foundation.

“We’ve already seen an uptick in contributions to

Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language

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Achieving Uber’s goal of bringing reliable transportation to everyone requires effortless prediction and optimization at every turn. Opportunities range from matching riders to drivers, to suggesting optimal routes, finding sensible pool combinations, and even creating the next generation of intelligent

Research Papers

Joint Mapping and Calibration via Differentiable Sensor Fusion

J. Chen, F. Obermeyer, V. Lyapunov, L. Gueguen, N. Goodman
We leverage automatic differentiation (AD) and probabilistic programming to develop an end-to-end optimization algorithm for batch triangulation of a large number of unknown objects. Given noisy detections extracted from noisily geo-located street level imagery without depth information, we jointly estimate the number and location of objects of different types, together with parameters for sensor noise characteristics and prior distribution of objects conditioned on side information. [...] [PDF]
Computing Research Repository (CoRR), 2018

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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