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Found 20 papers, 6 papers with code
Synthesizing Programs with Continuous Optimization
In this paper, we present a novel formulation of program synthesis as a continuous optimization problem and use a state-of-the-art evolutionary approach, known as Covariance Matrix Adaptation Evolution Strategy to solve the problem.
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
Toward Code Generation: A Survey and Lessons from Semantic Parsing
With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages.
ControlFlag: A Self-Supervised Idiosyncratic Pattern Detection System for Software Control Structures
Software debugging has been shown to utilize upwards of half of developers' time.
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.
(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.
Software Language Comprehension using a Program-Derived Semantics Graph
To continue to advance this research, we present the program-derived semantics graph, a new graphical structure to capture semantics of code.
Context-Aware Parse Trees
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing program \emph{structure} rather than program \emph{syntax}.
An Abstraction-Based Framework for Neural Network Verification
In this paper, we propose a framework that can enhance neural network verification techniques by using over-approximation to reduce the size of the network - thus making it more amenable to verification.
Learning Fitness Functions for Machine Programming
The problem of automatic software generation is known as Machine Programming.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
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.
Toward Scalable Verification for Safety-Critical Deep Networks
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.
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 Range-Based Anomaly Detection
Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point.
Paranom: A Parallel Anomaly Dataset Generator
In this paper, we present Paranom, a parallel anomaly dataset generator.
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.
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms
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 human guidance.
