Search Results for author: David Wingate

Found 13 papers, 2 papers with code

Towards Neural Programming Interfaces

1 code implementation NeurIPS 2020 Zachary C. Brown, Nathaniel Robinson, David Wingate, Nancy Fulda

It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models.

Language Modelling Text Generation

Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads

no code implementations3 Jan 2020 Erich Mielke, Eric Townsend, David Wingate, Marc D. Killpack

We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad.

Wasserstein Neural Processes

no code implementations1 Oct 2019 Andrew Carr, Jared Nielsen, David Wingate

Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions.

Video Extrapolation with an Invertible Linear Embedding

no code implementations1 Mar 2019 Robert Pottorff, Jared Nielsen, David Wingate

We predict future video frames from complex dynamic scenes, using an invertible neural network as the encoder of a nonlinear dynamic system with latent linear state evolution.

Predict Future Video Frames

Graph Neural Processes: Towards Bayesian Graph Neural Networks

no code implementations26 Feb 2019 Andrew Carr, David Wingate

We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes.


Nested Reasoning About Autonomous Agents Using Probabilistic Programs

no code implementations4 Dec 2018 Iris Rubi Seaman, Jan-Willem van de Meent, David Wingate

As autonomous agents become more ubiquitous, they will eventually have to reason about the plans of other agents, which is known as theory of mind reasoning.

Probabilistic Programming

Embedding Grammars

no code implementations14 Aug 2018 David Wingate, William Myers, Nancy Fulda, Tyler Etchart

Classic grammars and regular expressions can be used for a variety of purposes, including parsing, intent detection, and matching.

Intent Detection Word Embeddings

Probabilistic programs for inferring the goals of autonomous agents

1 code implementation17 Apr 2017 Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka

Intelligent systems sometimes need to infer the probable goals of people, cars, and robots, based on partial observations of their motion.

Automated Variational Inference in Probabilistic Programming

no code implementations7 Jan 2013 David Wingate, Theophane Weber

We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs.

Probabilistic Programming Variational Inference

Nonstandard Interpretations of Probabilistic Programs for Efficient Inference

no code implementations NeurIPS 2011 David Wingate, Noah Goodman, Andreas Stuhlmueller, Jeffrey M. Siskind

Probabilistic programming languages allow modelers to specify a stochastic process using syntax that resembles modern programming languages.

Probabilistic Programming

Nonparametric Bayesian Policy Priors for Reinforcement Learning

no code implementations NeurIPS 2010 Finale Doshi-Velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum

We consider reinforcement learning in partially observable domains where the agent can query an expert for demonstrations.

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