Papers Program Synthesis
“Program Synthesis” 태그가 달린 논문 538편 · 필터 해제
Program Synthesis with Pragmatic Communication
Program synthesis techniques construct or infer programs from user-provided specifications, such as input-output examples. Yet most specifications, especially those given by end-users, leave the synthesis problem radical…
Inductive BiasProgram SynthesisLearning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context
We explore learning web-based tasks from a human teacher through natural language explanations and a single demonstration. Our approach investigates a new direction for semantic parsing that models explaining a demonstra…
Language AcquisitionProgram SynthesisSemantic ParsingInformation-theoretic User Interaction: Significant Inputs for Program Synthesis
Programming-by-example technologies are being deployed in industrial products for real-time synthesis of various kinds of data transformations. These technologies rely on the user to provide few representative examples o…
ClusteringProgram SynthesisNeural Program Synthesis with a Differentiable Fixer
We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer. Our approach is inspired from the fact that human developers seldom get their…
DecoderProgram SynthesisIReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis
In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this descrip…
Computer SecurityProgram SynthesisDreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We pres…
Drawing PicturesProgram inductionProgram SynthesisPLANS: Robust Program Learning from Neurally Inferred Specifications
Recent years have seen the rise of statistical program learning based on neural models as an alternative to traditional rule-based systems for programming by example. Rule-based approaches offer correctness guarantees in…
Program SynthesisGraph-based, Self-Supervised Program Repair from Diagnostic Feedback
We consider the problem of learning to repair programs from diagnostic feedback (e.g., compiler error messages). Program repair is challenging for two reasons: First, it requires reasoning and tracking symbols across sou…
Code GenerationDiagnosticGraph LearningGraph Neural Network+3Guiding Program Synthesis by Learning to Generate Examples
A key challenge of existing program synthesizers is ensuring that the synthesized program generalizes well. This can be difficult to achieve as the specification provided by the end user is often limited, containing as f…
Program SynthesisKnowledge Refactoring for Inductive Program Synthesis
Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the \textit{knowledge refactoring} p…
Inductive logic programmingProgram inductionProgram SynthesisLearning large logic programs by going beyond entailment
A major challenge in inductive logic programming (ILP) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. This approach is limited b…
Inductive logic programmingProgram SynthesisCreating Synthetic Datasets via Evolution for Neural Program Synthesis
Program synthesis is the task of automatically generating a program consistent with a given specification. A natural way to specify programs is to provide examples of desired input-output behavior, and many current progr…
Program SynthesisTF-Coder: Program Synthesis for Tensor Manipulations
The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models. However, these libraries …
Deep LearningEnumerative SearchProgram SynthesisLearning Compositional Rules via Neural Program Synthesis
Many aspects of human reasoning, including language, require learning rules from very little data. Humans can do this, often learning systematic rules from very few examples, and combining these rules to form composition…
Meta-LearningProgram SynthesisIncremental Sampling Without Replacement for Sequence Models
Sampling is a fundamental technique, and sampling without replacement is often desirable when duplicate samples are not beneficial. Within machine learning, sampling is useful for generating diverse outputs from a traine…
Combinatorial OptimizationProgram SynthesisImproving Molecular Design by Stochastic Iterative Target Augmentation
Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models from data may be challenging due to the …
Program SynthesisEvaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis
Implementing enterprise process automation often requires significant technical expertise and engineering effort. It would be beneficial for non-technical users to be able to describe a business process in natural langua…
Program SynthesisUnsupervised Program Synthesis for Images By Sampling Without Replacement
Program synthesis has emerged as a successful approach to the image parsing task. Most prior works rely on a two-step scheme involving supervised pretraining of a Seq2Seq model with synthetic programs followed by reinfor…
Program SynthesisReinforcement Learning (RL)CounterExample Guided Neural Synthesis
Program synthesis is the generation of a program from a specification. Correct synthesis is difficult, and methods that provide formal guarantees suffer from scalability issues. On the other hand, neural networks are abl…
Program SynthesisComparison of Syntactic and Semantic Representations of Programs in Neural Embeddings
Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new tool. This work aims to be the first empir…
Program Synthesis