490 citations · 1k across the 74 of their papers we have counts for
6 papers · 1 filter
Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages
Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka
Automatic differentiation (AD) aims to compute derivatives of user-defined functions, but in Turing-complete languages, this simple specification does not fully capture AD's behavi…
3DP3: 3D Scene Perception via Probabilistic Programming
Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg +6
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent the 3…
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy, Ingmar Posner, Tim Barfoot +17
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning metho…
Hierarchical Infinite Relational Model
Feras A. Saad, Vikash K. Mansinghka
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of re…
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind
Arwa Alanqary, Gloria Z. Lin, Joie Le +3
When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teac…
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference
Sam Witty, David Jensen, Vikash Mansinghka
A growing family of approaches to causal inference rely on Bayesian formulations of assumptions that go beyond causal graph structure. For example, Bayesian approaches have been de…