Search Results for author:
Found 9 papers, 0 papers with code
Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold
This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train.
LLMs for Test Input Generation for Semantic Caches
Our novel approach uses the reasoning capabilities of LLMs to 1) adapt queries to the domain, 2) synthesise subtle variations to queries, and 3) evaluate the synthesised test dataset.
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
In this paper, we introduce ML-On-Rails, a protocol designed to safeguard ML models, establish a well-defined endpoint interface for different ML tasks, and clear communication between ML providers and ML consumers (software engineers).
An empirical study of automatic wildlife detection using drone thermal imaging and object detection
Specifically, we collect a realistic dataset of drone-derived wildlife thermal detections.
Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems
[Method] In this paper, we present a new framework developed based on human-centered AI guidelines and a user survey to aid in collecting requirements for human-centered AI-based software.
Deep Learning Methods for Credit Card Fraud Detection
This paper presents a thorough study of deep learning methods for the credit card fraud detection problem and compare their performance with various machine learning algorithms on three different financial datasets.
Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components
Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning.
Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow
The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision.
Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services
Multiple vendors now offer this technology as cloud services and developers want to leverage these advances to provide value to end-users.
