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Found 15 papers, 5 papers with code
Mind the Data Gap: Bridging LLMs to Enterprise Data Integration
We show that, once these techniques are deployed, the performance on enterprise data becomes on par with that of public data.
BEAVER: An Enterprise Benchmark for Text-to-SQL
However, how these results transfer to enterprise settings is unclear because tables in enterprise databases might differ substantially from web tables in structure and content.
Making LLMs Work for Enterprise Data Tasks
Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content.
CascadeServe: Unlocking Model Cascades for Inference Serving
In the offline phase, the system pre-computes a gear plan that specifies how to serve inferences online.
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.
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.
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.
(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.
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.
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.
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.
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.
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.
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.
