10 papers
Vendi Novelty Scores for Out-of-Distribution Detection
Amey P. Pasarkar, Adji Bousso Dieng
Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems. Existing post-hoc detectors typically rely on model confidence scores or likeli…
Building informative materials datasets beyond targeted objectives
Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6
Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaigns. In practice, researchers p…
Are neural scaling laws leading quantum chemistry astray?
Siwoo Lee, Adji Bousso Dieng
Neural scaling laws are driving the machine learning community toward training ever-larger foundation models across domains, assuring high accuracy and transferable representations…
Applications of the Vendi score in genomic epidemiology
Bjarke Frost Nielsen, Amey P. Pasarkar, Qiqi Yang +2
The Vendi score (VS), a diversity metric recently conceived in the context of machine learning, with applications in a wide range of fields, has a few distinct advantages over the…
A Unified and Predictive Measure of Functional Diversity
Adji Bousso Dieng, Amey Pasarkar
Despite the critical role of functional diversity (FD) in understanding ecological systems and processes, its robust quantification remains a significant challenge. A long-held vie…
Rethinking Ecological Measures Of Functional Diversity
Ines Meraoumia, Adji Bousso Dieng
Understanding functional diversity, the range and variability of species' roles and actions within their communities, is key to predicting and preserving the functions that sustain…