20 citations · 22 across the 3 of their papers we have counts for
5 papers · 1 filter
Software Language Comprehension using a Program-Derived Semantics Graph
Roshni G. Iyer, Yizhou Sun, Wei Wang +1
Traditional code transformation structures, such as abstract syntax trees (ASTs), conteXtual flow graphs (XFGs), and more generally, compiler intermediate representations (IRs), ma…
Precision and Recall for Range-Based Anomaly Detection
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul +2
Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point. In this paper, we present a new mathematical model to…
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul +2
This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.
Toward Scalable Verification for Safety-Critical Deep Networks
Lindsey Kuper, Guy Katz, Justin Gottschlich +3
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Form…
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms
Kory Becker, Justin Gottschlich
In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal hu…