76 citations · 252 across the 16 of their papers we have counts for
5 papers · 1 filter
Top-Down Synthesis for Library Learning
Matthew Bowers, Theo X. Olausson, Lionel Wong +4
This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain spe…
Toward Trustworthy Neural Program Synthesis
Darren Key, Wen-Ding Li, Kevin Ellis
We develop an approach to estimate the probability that a program sampled from a large language model is correct. Given a natural language description of a programming problem, our…
From Perception to Programs: Regularize, Overparameterize, and Amortize
Hao Tang, Kevin Ellis
Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into…
Efficient Pragmatic Program Synthesis with Informative Specifications
Saujas Vaduguru, Kevin Ellis, Yewen Pu
Providing examples is one of the most common way for end-users to interact with program synthesizers. However, program synthesis systems assume that examples consistent with the pr…
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Kensen Shi, Hanjun Dai, Kevin Ellis +1
Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to…