11 citations · 12 across the 3 of their papers we have counts for
10 papers
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
Ripon K. Saha, Akira Ura, Sonal Mahajan +6
Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However,…
Distributed Symbolic Execution using Test-Depth Partitioning
Shikhar Singh, Sarfraz Khurshid
Symbolic execution is a classic technique for systematic bug finding, which has seen many applications in recent years but remains hard to scale. Recent work introduced ranged symb…
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Marko Vasic, Cameron Chalk, Sarfraz Khurshid +1
Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and ot…
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
Muhammad Usman, Wenxi Wang, Kaiyuan Wang +3
This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A…
CRNs Exposed: Systematic Exploration of Chemical Reaction Networks
Marko Vasic, David Soloveichik, Sarfraz Khurshid
Formal methods have enabled breakthroughs in many fields, such as in hardware verification, machine learning and biological systems. The key object of interest in systems biology,…
CRN++: Molecular Programming Language
Marko Vasic, David Soloveichik, Sarfraz Khurshid
Synthetic biology is a rapidly emerging research area, with expected wide-ranging impact in biology, nanofabrication, and medicine. A key technical challenge lies in embedding comp…