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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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5 results for “Programming-by-Example”

Data Generation for Neural Programming by Examplecs.LG

Paper introduces a novel method to generate diverse inputs for neural programming by example.

problem Synthesizing programs from input/output pairs using machine learning.

method Uses an SMT solver to generate diverse input-output pairs.

result Generated inputs improve model performance and generalization.

Programming by example Machine learning Synthetic data SMT solver Generalization

2019-11-06abs ↗pdf ↗

TF-Coder: Program Synthesis for Tensor Manipulationscs.PL

TF-Coder simplifies tensor manipulation programming in TensorFlow.

problem Difficulty in programming with TensorFlow due to steep learning curve.

method Bottom-up weighted enumerative search with value-based pruning and type/value filtering.

result Solves 63 out of 70 real-world tasks within 5 minutes.

TensorFlow Programming by example Tensor manipulation Deep learning Value-based pruning

2020-03-19abs ↗pdf ↗

Grammar Filtering For Syntax-Guided Synthesiscs.LG

System uses machine learning and automated reasoning to speed up PBE synthesis.

problem Slow synthesis in PBE due to domain-specific knowledge and large training datasets.

method Preprocess SyGuS PBE problems with a neural network to reduce search space, then use automated reasoning for faster solution.

result System outperforms all competing tools in the 2019 SyGuS Competition for the PBE Strings track by 47.65%.

Programming-by-Example Synthesis Machine Learning Automated Reasoning SyGuS

2020-02-07abs ↗pdf ↗

Neural Guided Constraint Logic Programming for Program Synthesiscs.LG

Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. Crucially, the neural model uses miniKanren's i…

2018-09-08abs ↗pdf ↗

PLANS: Robust Program Learning from Neurally Inferred Specificationscs.LG

PLANS synthesizes programs from noisy inputs using neural specs and filtering.

problem Synthesizing robust programs from noisy, raw inputs.

method Hybrid model combining neural extraction and rule-based synthesis with noise filtering.

result State-of-the-art performance in diverse environments with no ground-truth training.

program synthesis neural models rule-based systems filtering unsupervised learning

2020-06-05abs ↗pdf ↗