76 citations · 252 across the 16 of their papers we have counts for
16 papers
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language
Kevin Ellis
A core tension in models of concept learning is that the model must carefully balance the tractability of inference against the expressivity of the hypothesis class. Humans, howeve…
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
Kensen Shi, Hanjun Dai, Wen-Ding Li +2
Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior…
Discovering Quantum Circuit Components with Program Synthesis
Leopoldo Sarra, Kevin Ellis, Florian Marquardt
Despite rapid progress in the field, it is still challenging to discover new ways to take advantage of quantum computation: all quantum algorithms need to be designed by hand, and…
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…
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
Tuan Anh Le, Katherine M. Collins, Luke Hewitt +4
Modeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in…
Leveraging Language to Learn Program Abstractions and Search Heuristics
Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum +1
Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learni…
Learning abstract structure for drawing by efficient motor program induction
Lucas Y. Tian, Kevin Ellis, Marta Kryven +1
Humans flexibly solve new problems that differ qualitatively from those they were trained on. This ability to generalize is supported by learned concepts that capture structure com…
Program Synthesis with Pragmatic Communication
Yewen Pu, Kevin Ellis, Marta Kryven +2
Program synthesis techniques construct or infer programs from user-provided specifications, such as input-output examples. Yet most specifications, especially those given by end-us…
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
Kevin Ellis, Catherine Wong, Maxwell Nye +6
Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, a…
Write, Execute, Assess: Program Synthesis with a REPL
Kevin Ellis, Maxwell Nye, Yewen Pu +3
We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs. We equip the search p…
Learning to Infer and Execute 3D Shape Programs
Yonglong Tian, Andrew Luo, Xingyuan Sun +4
Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape struct…
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama +1
We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program…