Program Synthesis

90 papers with code • 4 benchmarks • 5 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Neural Program Synthesis with Priority Queue Training

tensorflow/models 10 Jan 2018

Models and examples built with TensorFlow

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

crazydonkey200/neural-symbolic-machines NeurIPS 2018

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.

DeepCoder: Learning to Write Programs

HiroakiMikami/deep-coder 7 Nov 2016

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning.

RobustFill: Neural Program Learning under Noisy I/O

amitz25/PCCoder ICML 2017

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.

HOUDINI: Lifelong Learning as Program Synthesis

capergroup/houdini NeurIPS 2018

We present a neurosymbolic framework for the lifelong learning of algorithmic tasks that mix perception and procedural reasoning.

Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural Representations

ckzbullbullet/TP-N2F ICML 2020

The encoder of TP-N2F employs TPR `binding' to encode natural-language symbolic structure in vector space and the decoder uses TPR `unbinding' to generate, in symbolic space, a sequential program represented by relational tuples, each consisting of a relation (or operation) and a number of arguments.

Improving Molecular Design by Stochastic Iterative Target Augmentation

yangkevin2/icml2020-stochastic-iterative-target-augmentation ICML 2020

The property predictor is then used as a likelihood model for filtering candidate structures from the generative model.

TF-Coder: Program Synthesis for Tensor Manipulations

google-research/tensorflow-coder NeurIPS Workshop CAP 2020

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models.

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

michiyasunaga/DrRepair ICML 2020

Second, we present a self-supervised learning paradigm for program repair that leverages unlabeled programs available online to create a large amount of extra program repair examples, which we use to pre-train our models.

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

CatherineWong/dreamcoder 15 Jun 2020

It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.