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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
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 by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer.
Forgetting to learn logic programs
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
In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge.
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.
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.
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
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We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.
Automatic Discovery of Interpretable Planning Strategies
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.
P-Tree Programming
From this prototype tree we form program instances which we evaluate on a given problem.