Program induction

22 papers with code • 0 benchmarks • 1 datasets

Generating program code for domain-specific tasks


Most implemented papers

DeepProbLog: Neural Probabilistic Logic Programming

ml-kuleuven/deepproblog NeurIPS 2018

We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.

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.

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.

Learning a Natural Language Interface with Neural Programmer

tensorflow/models 28 Nov 2016

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.

Forgetting to learn logic programs

metagol/metagol 15 Nov 2019

To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK.

Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems

deepmind/AQuA 11 May 2017

Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer.

P-Tree Programming

coesch/ptree 12 Jul 2017

From this prototype tree we form program instances which we evaluate on a given problem.

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

StanfordVL/arxivbot 4 Oct 2017

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.

Recent Advances in Neural Program Synthesis

qu-arx/arx-inf 7 Feb 2018

In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence.

Playgol: learning programs through play

metagol/metagol 18 Apr 2019

In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge.