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Found 11 papers, 5 papers with code
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
This is, in part, due to the emergence of a wide range of novel techniques in machine learning.
Precision and Recall for Range-Based Anomaly Detection
Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point.
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.
Precision and Recall for Time Series
Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time.
The Three Pillars of Machine Programming
In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research.
LISA: Towards Learned DNA Sequence Search
Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics.
MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure
Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection.
(Updated submission 11/20/2020) MISIM: A Novel Code Similarity System
First, MISIM uses a novel context-aware semantic structure (CASS), which is designed to aid in lifting semantic meaning from code syntax.
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many experimental research domains.
A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification
Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task.
Extract-Transform-Load for Video Streams
We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL.
