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Published Date

Project

CHET

CHET is a compiler and runtime that automates many parts of this process for neural network inference tasks. The compiler applies transformations based on a framework of symbolic analysis passes.

Video

TLA+ Specifications of the Consistency Guarantees Provided by Cosmos DB

November 1, 2018 | Murat Demirbas

Microsoft Azure Cosmos DB provides 5 well defined operation consistency properties to the clients: strong consistency, bounded staleness, session consistency, consistent prefix, and eventual consistency. Here we provide client-centric TLA+ specifications of these properties to…

TLA+ Specifications of the Consistency Guarantees Provided by Cosmos DB

01:19:44

Publication

How Should Compilers Explain Problems to Developers?

Titus Barik, Denae Ford, Emerson Murphy-Hill, Chris Parnin

ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) | November 2018

Publication

Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces

Jordan Henkel, Shuvendu Lahiri, Ben Liblit, Thomas Reps

Proceedings of the 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ’18) | November 2018

Publication

Verified Three-Way Program Merge

Marcelo Sousa, Isil Dillig, Shuvendu Lahiri

Object-Oriented Programming, Systems, Languages & Applications Conference (OOPSLA 2018) | November 2018

Project Project

Publication

LinkSO: A Benchmark for Learning to Retrieve Similar Question-Answer Pairs on Software Development Forums

Xueqing Liu, Chi Wang, Yue Leng, Chengxiang Zhai

ESEC/FSE Workshop on NLP for Software Engineering (NL4SE’18) | November 2018

Publication

Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution

Matthew Clarke, Steven Woodhouse, Nir Piterman, Benjamin Hall, Jasmin Fisher

Automated Reasoning for Systems Biology and Medicine, in press | October 2018

Video

How to Obtain and Run Light and Efficient Deep Learning Networks

October 26, 2018 | Yiran Chen

Fast growth of the computation cost associated with training and testing of deep neural networks (DNNs) inspired various acceleration techniques. Reducing topological complexity and simplifying data representation of neural networks are two approaches that popularly…