
-
Learning to refer informatively by amortizing pragmatic reasoning
A hallmark of human language is the ability to effectively and efficient...
read it
-
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks
A key property of linguistic conventions is that they hold over an entir...
read it
-
Characterizing the dynamics of learning in repeated reference games
The language we use over the course of conversation changes as we establ...
read it
-
Continual adaptation for efficient machine communication
To communicate with new partners in new contexts, humans rapidly form ne...
read it
-
Analyzing machine-learned representations: A natural language case study
As modern deep networks become more complex, and get closer to human-lik...
read it
-
Learning to Explain: Answering Why-Questions via Rephrasing
Providing plausible responses to why questions is a challenging but crit...
read it
-
ShapeGlot: Learning Language for Shape Differentiation
In this work we explore how fine-grained differences between the shapes ...
read it
-
When redundancy is rational: A Bayesian approach to 'overinformative' referring expressions
Referring is one of the most basic and prevalent uses of language. How d...
read it
-
Applying Probabilistic Programming to Affective Computing
Affective Computing is a rapidly growing field spurred by advancements i...
read it
-
Joint Mapping and Calibration via Differentiable Sensor Fusion
We leverage automatic differentiation (AD) and probabilistic programming...
read it
-
Pyro: Deep Universal Probabilistic Programming
Pyro is a probabilistic programming language built on Python as a platfo...
read it
-
An Incremental Iterated Response Model of Pragmatics
Recent Iterated Response (IR) models of pragmatics conceptualize languag...
read it
-
Speakers account for asymmetries in visual perspective so listeners don't have to
Debates over adults' theory of mind use have been fueled by surprising f...
read it
-
Planning, Inference and Pragmatics in Sequential Language Games
We study sequential language games in which two players, each with priva...
read it
-
Evaluating Compositionality in Sentence Embeddings
An important frontier in the quest for human-like AI is compositional se...
read it
-
DisSent: Sentence Representation Learning from Explicit Discourse Relations
Sentence vectors represent an appealing approach to meaning: learn an em...
read it
-
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
read it
-
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
We present a model of pragmatic referring expression interpretation in a...
read it
-
Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
read it
-
Deep Amortized Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are a powerful modeling tool,...
read it
-
A pragmatic theory of generic language
Generalizations about categories are central to human understanding, and...
read it
-
Learning to Generate Compositional Color Descriptions
The production of color language is essential for grounded language gene...
read it
-
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) ...
read it
-
Learning the Preferences of Ignorant, Inconsistent Agents
An important use of machine learning is to learn what people value. What...
read it
-
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs
Many practical techniques for probabilistic inference require a sequence...
read it
-
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Lightweight, source-to-source transformation approaches to implementing ...
read it
-
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
We describe a dynamic programming algorithm for computing the marginal d...
read it
-
Inducing Probabilistic Programs by Bayesian Program Merging
This report outlines an approach to learning generative models from data...
read it