13 citations · 21 across the 4 of their papers we have counts for
4 papers
Enabling Calibration In The Zero-Shot Inference of Large Vision-Language Models
Will LeVine, Benjamin Pikus, Pranav Raja +1
Calibration of deep learning models is crucial to their trustworthiness and safe usage, and as such, has been extensively studied in supervised classification models, with methods…
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Haoyin Xu, Kaleab A. Kinfu, Will LeVine +9
Deep networks and decision forests (such as random forests and gradient boosted trees) are the leading machine learning methods for structured and tabular data, respectively. Many…
Simple Lifelong Learning Machines
Jayanta Dey, Joshua T. Vogelstein, Hayden S. Helm +13
In lifelong learning, data are used to improve performance not only on the present task, but also on past and future (unencountered) tasks. While typical transfer learning algorith…
Accurate Layerwise Interpretable Competence Estimation
Vickram Rajendran, William LeVine
Estimating machine learning performance 'in the wild' is both an important and unsolved problem. In this paper, we seek to examine, understand, and predict the pointwise competence…