2.3k citations · 4.7k across the 88 of their papers we have counts for
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Inducing Interpretable Representations with Variational Autoencoders
N. Siddharth, Brooks Paige, Alban Desmaison +5
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…
Deep Amortized Inference for Probabilistic Programs
Daniel Ritchie, Paul Horsfall, Noah D. Goodman
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference i…
Practical optimal experiment design with probabilistic programs
Long Ouyang, Michael Henry Tessler, Daniel Ly +1
Scientists often run experiments to distinguish competing theories. This requires patience, rigor, and ingenuity - there is often a large space of possible experiments one could ru…
The Language of Generalization
Michael Henry Tessler, Noah D. Goodman
Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly", "John hikes", "Fire makes smoke"). Though found in every language and emerg…
Learning to Generate Compositional Color Descriptions
Will Monroe, Noah D. Goodman, Christopher Potts
The production of color language is essential for grounded language generation. Color descriptions have many challenging properties: they can be vague, compositionally complex, and…
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Daniel Ritchie, Anna Thomas, Pat Hanrahan +1
Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many sample…