96 citations · 350 across the 21 of their papers we have counts for
6 papers · 1 filter
The Three Pillars of Machine Programming
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul +6
In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention…
Precision and Recall for Time Series
Nesime Tatbul, Tae Jun Lee, Stan Zdonik +2
Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-base…
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…
Paranom: A Parallel Anomaly Dataset Generator
Justin Gottschlich
In this paper, we present Paranom, a parallel anomaly dataset generator. We discuss its design and provide brief experimental results demonstrating its usefulness in improving the…