From word models to world models: Translating from natural language to the probabilistic language of thought
How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …
meaning from language--and how can we leverage a theory of linguistic meaning to build …
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
G Papamakarios, D Sterratt… - The 22nd international …, 2019 - proceedings.mlr.press
Abstract We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but simulating data from …
inference in simulator models, where the likelihood is intractable but simulating data from …
Gen: a general-purpose probabilistic programming system with programmable inference
Although probabilistic programming is widely used for some restricted classes of statistical
models, existing systems lack the flexibility and efficiency needed for practical use with more …
models, existing systems lack the flexibility and efficiency needed for practical use with more …
Online bayesian goal inference for boundedly rational planning agents
T Zhi-Xuan, J Mann, T Silver… - Advances in neural …, 2020 - proceedings.neurips.cc
People routinely infer the goals of others by observing their actions over time. Remarkably,
we can do so even when those actions lead to failure, enabling us to assist others when we …
we can do so even when those actions lead to failure, enabling us to assist others when we …
Neural density estimation and likelihood-free inference
G Papamakarios - arXiv preprint arXiv:1910.13233, 2019 - arxiv.org
I consider two problems in machine learning and statistics: the problem of estimating the
joint probability density of a collection of random variables, known as density estimation, and …
joint probability density of a collection of random variables, known as density estimation, and …
Probabilistic programming with programmable inference
We introduce inference metaprogramming for probabilistic programming languages,
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …
Inferring the goals of communicating agents from actions and instructions
When humans cooperate, they frequently coordinate their activity through both verbal
communication and non-verbal actions, using this information to infer a shared goal and …
communication and non-verbal actions, using this information to infer a shared goal and …
Probabilistic programming with stochastic probabilities
We present a new approach to the design and implementation of probabilistic programming
languages (PPLs), based on the idea of stochastically estimating the probability density …
languages (PPLs), based on the idea of stochastically estimating the probability density …
PDDL. jl: An extensible interpreter and compiler interface for fast and flexible AI planning
T Zhi-Xuan - 2022 - dspace.mit.edu
The Planning Domain Definition Language (PDDL) is a formal specification language for
symbolic planning problems and domains that is widely used by the AI planning community …
symbolic planning problems and domains that is widely used by the AI planning community …
Solving the baby intuitions benchmark with a hierarchically bayesian theory of mind
To facilitate the development of new models to bridge the gap between machine and human
social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv: 2102.11938) …
social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv: 2102.11938) …