76 research outputs found
Running Probabilistic Programs Backwards
Many probabilistic programming languages allow programs to be run under
constraints in order to carry out Bayesian inference. Running programs under
constraints could enable other uses such as rare event simulation and
probabilistic verification---except that all such probabilistic languages are
necessarily limited because they are defined or implemented in terms of an
impoverished theory of probability. Measure-theoretic probability provides a
more general foundation, but its generality makes finding computational content
difficult.
We develop a measure-theoretic semantics for a first-order probabilistic
language with recursion, which interprets programs as functions that compute
preimages. Preimage functions are generally uncomputable, so we derive an
abstract semantics. We implement the abstract semantics and use the
implementation to carry out Bayesian inference, stochastic ray tracing (a rare
event simulation), and probabilistic verification of floating-point error
bounds.Comment: 26 pages, ESOP 2015 (to appear
"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
Code-generating large language models translate natural language into code.
However, only a small portion of the infinite space of naturalistic utterances
is effective at guiding code generation. For non-expert end-user programmers,
learning this is the challenge of abstraction matching. We examine this
challenge in the specific context of data analysis in spreadsheets, in a system
that maps the users natural language query to Python code using the Codex
generator, executes the code, and shows the result. We propose grounded
abstraction matching, which bridges the abstraction gap by translating the code
back into a systematic and predictable naturalistic utterance. In a
between-subjects, think-aloud study (n=24), we compare grounded abstraction
matching to an ungrounded alternative based on previously established query
framing principles. We find that the grounded approach improves end-users'
understanding of the scope and capabilities of the code-generating model, and
the kind of language needed to use it effectively
Edge Inference for Image Interpolation
Abstract — Image interpolation algorithms try to fit a function to a matrix of samples in a “natural-looking ” way. This paper presents edge inference, an algorthm that does this by mixing neural network regression with standard image interpolation techniques. Results on gray level images are presented. Extension into RGB color space and additional applications of the algorithm are discussed. I
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