84 citations · 122 across the 4 of their papers we have counts for
6 papers
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
Joseph D. Romano, Trang T. Le, William La Cava +7
Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that…
Is deep learning necessary for simple classification tasks?
Joseph D. Romano, Trang T. Le, Weixuan Fu +1
Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little gu…
Evaluating recommender systems for AI-driven biomedical informatics
William La Cava, Heather Williams, Weixuan Fu +3
Motivation: Many researchers with domain expertise are unable to easily apply machine learning to their bioinformatics data due to a lack of machine learning and/or coding expertis…
Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients
Trang T. Le, Nigel O. Blackwood, Jaclyn N. Taroni +2
Clusters of differentiation (CD) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibo…
Investigating the Parameter Space of Evolutionary Algorithms
Moshe Sipper, Weixuan Fu, Karuna Ahuja +1
The practice of evolutionary algorithms involves the tuning of many parameters. How big should the population be? How many generations should the algorithm run? What is the (tourna…
A System for Accessible Artificial Intelligence
Randal S. Olson, Moshe Sipper, William La Cava +7
While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowled…