Search Results for author: Petros Maniatis

Found 6 papers, 4 papers with code

SpreadsheetCoder: Formula Prediction from Semi-structured Context

1 code implementation26 Jun 2021 Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou

In this work, we present the first approach for synthesizing spreadsheet formulas from tabular context, which includes both headers and semi-structured tabular data.

Program Synthesis

Neural Program Synthesis with a Differentiable Fixer

no code implementations19 Jun 2020 Matej Balog, Rishabh Singh, Petros Maniatis, Charles Sutton

We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer.

Program Synthesis

Global Relational Models of Source Code

1 code implementation ICLR 2020 Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber

By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation.

Variable misuse

Learning and Evaluating Contextual Embedding of Source Code

2 code implementations ICML 2020 Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi

We fine-tune CuBERT on our benchmark tasks, and compare the resulting models to different variants of Word2Vec token embeddings, BiLSTM and Transformer models, as well as published state-of-the-art models, showing that CuBERT outperforms them all, even with shorter training, and with fewer labeled examples.

Contextual Embedding for Source Code Exception type +6

Neural Program Repair by Jointly Learning to Localize and Repair

2 code implementations ICLR 2019 Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh

We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs.

Variable misuse

Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression

no code implementations NeurIPS 2010 Ling Huang, Jinzhu Jia, Bin Yu, Byung-Gon Chun, Petros Maniatis, Mayur Naik

Our two SPORE algorithms are able to build relationships between responses (e. g., the execution time of a computer program) and features, and select a few from hundreds of the retrieved features to construct an explicitly sparse and non-linear model to predict the response variable.

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