research
∙
05/28/2023
Reliable and Interpretable Drift Detection in Streams of Short Texts
Data drift is the change in model input data that is one of the key fact...
share
research
∙
05/14/2023
Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors
Machine learning (ML) solutions are prevalent in many applications. Howe...
research
∙
11/29/2022
Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora
The ability to compare the semantic similarity between text corpora is i...
research
∙
04/27/2022
High-quality Conversational Systems
Conversational systems or chatbots are an example of AI-Infused Applicat...
research
∙
01/02/2022
Experiment Based Crafting and Analyzing of Machine Learning Solutions
The crafting of machine learning (ML) based systems requires statistical...
research
∙
12/22/2021
Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation
Testing Machine Learning (ML) models and AI-Infused Applications (AIIAs)...
research
∙
11/10/2021
Automatically detecting data drift in machine learning classifiers
Classifiers and other statistics-based machine learning (ML) techniques ...
research
∙
10/24/2021
Detecting model drift using polynomial relations
Machine learning (ML) models serve critical functions, such as classifyi...
research
∙
10/11/2021
Density-based interpretable hypercube region partitioning for mixed numeric and categorical data
Consider a structured dataset of features, such as {SEX, INCOME, RACE, E...
research
∙
09/06/2021
Towards API Testing Across Cloud and Edge
API economy is driving the digital transformation of business applicatio...
research
∙
08/12/2021