20 papers with code • 0 benchmarks • 1 datasets
Generating program code for domain-specific tasks
These leaderboards are used to track progress in Program induction
Recently, two competing approaches for automatic program learning have received significant attention: (1) neural program synthesis, where a neural network is conditioned on input/output (I/O) examples and learns to generate a program, and (2) neural program induction, where a neural network generates new outputs directly using a latent program representation.
The main experimental result in this paper is that a single Neural Programmer model achieves 34. 2% accuracy using only 10, 000 examples with weak supervision.
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.
Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer.
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction.