Papers Program Synthesis
“Program Synthesis” 태그가 달린 논문 538편 · 필터 해제
Glass-Box Program Synthesis: A Machine Learning Approach
Recently proposed models which learn to write computer programs from data use either input/output examples or rich execution traces. Instead, we argue that a novel alternative is to use a glass-box loss function, given a…
BIG-bench Machine LearningProgram SynthesisA probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selec…
Program SynthesisregressionSymbolic RegressionLearning to Infer Graphics Programs from Hand-Drawn Images
We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neur…
Program SynthesisP-Tree Programming
We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we…
General ClassificationProgram inductionProgram Synthesisregression+1LoopInvGen: A Loop Invariant Generator based on Precondition Inference
We describe the LoopInvGen tool for generating loop invariants that can provably guarantee correctness of a program with respect to a given specification. LoopInvGen is an efficient implementation of the inference techni…
Program SynthesisLearning Disjunctions of Predicates
Let $F$ be a set of boolean functions. We present an algorithm for learning $F_\vee := \{\vee_{f\in S} f \mid S \subseteq F\}$ from membership queries. Our algorithm asks at most $|F| \cdot OPT(F_\vee)$ membership querie…
Program SynthesisTowards Synthesizing Complex Programs from Input-Output Examples
In recent years, deep learning techniques have been developed to improve the performance of program synthesis from input-output examples. Albeit its significant progress, the programs that can be synthesized by state-of-…
Program Synthesisreinforcement-learningReinforcement LearningReinforcement Learning (RL)RobustFill: Neural Program Learning under Noisy I/O
The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic program learning have received significant …
Program inductionProgram SynthesisSynthesizing Imperative Programs from Examples Guided by Static Analysis
We present a novel algorithm that synthesizes imperative programs for introductory programming courses. Given a set of input-output examples and a partial program, our algorithm generates a complete program that is consi…
Program SynthesisSummary - TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our key contribution is TerpreT, a domain-spe…
Probabilistic ProgrammingProgram inductionProgram SynthesisSampling for Bayesian Program Learning
Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data. Within this setting, we propose an algorithm that uses a symbolic solver to …
Program SynthesisTime Series Structure Discovery via Probabilistic Program Synthesis
There is a widespread need for techniques that can discover structure from time series data. Recently introduced techniques such as Automatic Bayesian Covariance Discovery (ABCD) provide a way to find structure within a …
ClusteringProgram SynthesisTime SeriesTime Series Analysis+1DeepCoder: Learning to Write Programs
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to predict properties of the program that gen…
Enumerative SearchProgram SynthesisDifferentiable Functional Program Interpreters
Programming by Example (PBE) is the task of inducing computer programs from input-output examples. It can be seen as a type of machine learning where the hypothesis space is the set of legal programs in some programming …
Program SynthesisLatent Attention For If-Then Program Synthesis
Automatic translation from natural language descriptions into programs is a longstanding challenging problem. In this work, we consider a simple yet important sub-problem: translation from textual descriptions to If-Then…
One-Shot LearningProgram SynthesisTranslationNeuro-Symbolic Program Synthesis
Recent years have seen the proposal of a number of neural architectures for the problem of Program Induction. Given a set of input-output examples, these architectures are able to learn mappings that generalize to new te…
Program inductionProgram SynthesisTerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop new machine learning…
BIG-bench Machine LearningProbabilistic ProgrammingProgram inductionProgram SynthesisUnsupervised Learning by Program Synthesis
We introduce an unsupervised learning algorithmthat combines probabilistic modeling with solver-based techniques for program synthesis.We apply our techniques to both a visual learning domain and a language learning prob…
Program Synthesis