no code implementations • 9 Jul 2023 • Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz, Oktie Hassanzadeh, Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Subhajit Chaudhury, Horst Samulowitz
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
no code implementations • 5 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.
1 code implementation • 29 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.
1 code implementation • 15 Sep 2021 • Ibrahim Abdelaziz, Julian Dolby, Jamie McCusker, Kavitha Srinivas
Code understanding is an increasingly important application of Artificial Intelligence.
1 code implementation • 25 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.
1 code implementation • 21 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.
1 code implementation • 5 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.
no code implementations • 10 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.
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