3 citations · 8 across the 10 of their papers we have counts for
11 papers
Can Cross Encoders Produce Useful Sentence Embeddings?
Haritha Ananthakrishnan, Julian Dolby, Harsha Kokel +2
Cross encoders (CEs) are trained with sentence pairs to detect relatedness. As CEs require sentence pairs at inference, the prevailing view is that they can only be used as re-rank…
PoTo: A Hybrid Andersen's Points-to Analysis for Python
Ingkarat Rak-amnouykit, Ana Milanova, Guillaume Baudart +2
As Python is increasingly being adopted for large and complex programs, the importance of static analysis for Python (such as type inference) grows. Unfortunately, static analysis…
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz +7
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful…
Out of style: Misadventures with LLMs and code style transfer
Karl Munson, Chih-Kai Ting, Serenity Wade +4
Like text, programs have styles, and certain programming styles are more desirable than others for program readability, maintainability, and performance. Code style transfer, howev…
LakeBench: Benchmarks for Data Discovery over Data Lakes
Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz +6
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery. Of particular importance to enterprises is the ability to…
Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning
Wenting Zhao, Ibrahim Abdelaziz, Julian Dolby +3
Dynamically typed languages such as Python have become very popular. Among other strengths, Python's dynamic nature and its straightforward linking to native code have made it the…
Automatically Debugging AutoML Pipelines using Maro: ML Automated Remediation Oracle (Extended Version)
Julian Dolby, Jason Tsay, Martin Hirzel
Machine learning in practice often involves complex pipelines for data cleansing, feature engineering, preprocessing, and prediction. These pipelines are composed of operators, whi…
Large Scale Generation of Labeled Type Data for Python
Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas
Recently, dynamically typed languages, such as Python, have gained unprecedented popularity. Although these languages alleviate the need for mandatory type annotations, types still…
A Scalable AutoML Approach Based on Graph Neural Networks
Mossad Helali, Essam Mansour, Ibrahim Abdelaziz +2
AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each lea…
Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding
Ibrahim Abdelaziz, Julian Dolby, Jamie McCusker +1
Code understanding is an increasingly important application of Artificial Intelligence. A fundamental aspect of understanding code is understanding text about code, e.g., documenta…
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
Ruchir Puri, David S. Kung, Geert Janssen +14
Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applicat…