Program Synthesis
85 papers with code • 4 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Program Synthesis
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HumanEval
CONCODE
SketchGraphs
SPoC
Subtasks
Most implemented papers
Neural Program Synthesis with Priority Queue Training
Models and examples built with TensorFlow
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
crazydonkey200/neural-symbolic-machines
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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
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
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
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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
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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
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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
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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
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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
It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.
