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Generating program code for domain-specific tasks

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Greatest papers with code

Learning a Natural Language Interface with Neural Programmer

28 Nov 2016tensorflow/models

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.

PROGRAM INDUCTION QUESTION ANSWERING

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

11 May 2017deepmind/AQuA

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

PROGRAM INDUCTION

Forgetting to learn logic programs

15 Nov 2019metagol/metagol

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

INDUCTIVE LOGIC PROGRAMMING MULTI-TASK LEARNING PROGRAM INDUCTION

Playgol: learning programs through play

18 Apr 2019metagol/metagol

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

INDUCTIVE LOGIC PROGRAMMING PROGRAM INDUCTION

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

15 Jun 2020ellisk42/ec

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

DRAWING PICTURES PROGRAM INDUCTION PROGRAM SYNTHESIS

RobustFill: Neural Program Learning under Noisy I/O

ICML 2017 amitz25/PCCoder

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.

PROGRAM INDUCTION PROGRAM SYNTHESIS

Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning

EMNLP 2020 DevinJake/MRL-CQA

Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.

KNOWLEDGE BASE QUESTION ANSWERING META REINFORCEMENT LEARNING PROGRAM INDUCTION

DeepProbLog: Neural Probabilistic Logic Programming

NeurIPS 2018 MarcRoigVilamala/DeepProbCEP

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

PROGRAM INDUCTION

Automatic Discovery of Interpretable Planning Strategies

24 May 2020RationalityEnhancement/InterpretableStrategyDiscovery

Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule.

DECISION MAKING IMITATION LEARNING PROGRAM INDUCTION

P-Tree Programming

12 Jul 2017coesch/ptree

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

PROGRAM INDUCTION PROGRAM SYNTHESIS