35 citations · 70 across the 4 of their papers we have counts for
6 papers
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…
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…