Search Results for author: Julian Dolby

Found 8 papers, 5 papers with code

Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning

no code implementations5 Jan 2023 Wenting Zhao, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas, Mossad Helali, Essam Mansour

We demonstrate the efficiency and usefulness of Serenity's analysis in two applications: code completion and automated machine learning.

Code Completion

A Scalable AutoML Approach Based on Graph Neural Networks

1 code implementation29 Oct 2021 Mossad Helali, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas

AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner.

AutoML Graph Generation +2

CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

1 code implementation25 May 2021 Ruchir Puri, David S. Kung, Geert Janssen, Wei zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, Frederick Reiss

In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.

BIG-bench Machine Learning Code Classification +1

A Toolkit for Generating Code Knowledge Graphs

1 code implementation21 Feb 2020 Ibrahim Abdelaziz, Julian Dolby, Jamie McCusker, Kavitha Srinivas

We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.

Code Search Image Classification +2

Merging datasets through deep learning

1 code implementation5 Sep 2018 Kavitha Srinivas, Abraham Gale, Julian Dolby

Our approach depends on (a) creating a deep learning model that maps surface forms of an entity into a set of vectors such that alternate forms for the same entity are closest in vector space, (b) indexing these vectors using a nearest neighbors algorithm to find the forms that can be potentially joined together.

Management Metric Learning

Ariadne: Analysis for Machine Learning Program

no code implementations10 May 2018 Julian Dolby, Avraham Shinnar, Allison Allain, Jenna Reinen

We report on Ariadne: applying a static framework, WALA, to machine learning code that uses TensorFlow.

Programming Languages

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