Source Code Generation Based On User Intention Using LSTM Networks
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Updated
Jan 6, 2018 - Python
Source Code Generation Based On User Intention Using LSTM Networks
UAV control with formal guarantees
Python module with Program Synthesis Techniques for NLP
Program synthesis tool. Read a list of strings and create a generator that sample strings from the same distribution as input strings. Focused on test cases of competitive programming problems.
This repository aims to synthesize programs in BrainFuck language. It takes Input-Output examples as input to generate program. The model uses Reinforcement learning to train model to generate model. The agent in RL task is generative RNN model, which samples programs in BF.
Re-implement DeepCoder (https://openreview.net/pdf?id=ByldLrqlx)
A public repository for "Write, Execute, Assess: Program Synthesis With a REPL" project by Kevin Ellis, Max Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama. Presented at NeurIPS 2019.
Many resources about program synthesis
PyTorch library for synthesizing programs from natural language
Multimodal Graph Network (MGN): Code repo, examples from the paper
A Framework for Model-Backed Generators. Backend for AutoPandas (https://autopandas.io)
Code for the paper "Learning Programs with arguments and sampling"
Generative Art Synthesizer - a python program that generates python programs that generates generative art
NeurIPS'21 Differentiable Program Synthesis
Baby Abstract Reasoning Corpus (BabyARC) dataset engine, for generating grid-world-based abstract reasoning tasks on a large scale.
PLUR (Programming-Language Understanding and Repair) is a collection of source code datasets suitable for graph-based machine learning. We provide scripts for downloading, processing, and loading the datasets. This is done by offering a unified API and data structures for all datasets.
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