2.3k citations · 4.7k across the 88 of their papers we have counts for
11 papers · 1 filter
Joint Mapping and Calibration via Differentiable Sensor Fusion
Jonathan P. Chen, Fritz Obermeyer, Vladimir Lyapunov +2
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 object…
Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao, Hongyu Ren, Arianna Yuan +3
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models i…
An Incremental Iterated Response Model of Pragmatics
Reuben Cohn-Gordon, Noah D. Goodman, Christopher Potts
Recent Iterated Response (IR) models of pragmatics conceptualize language use as a recursive process in which agents reason about each other to increase communicative efficiency. T…
Pyro: Deep Universal Probabilistic Programming
Eli Bingham, Jonathan P. Chen, Martin Jankowiak +7
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensio…
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
Mike Wu, Noah Goodman, Stefano Ermon
Stochastic optimization techniques are standard in variational inference algorithms. These methods estimate gradients by approximating expectations with independent Monte Carlo sam…
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
Mike Wu, Milan Mosse, Noah Goodman +1
In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these pl…