1 code implementation • 4 Mar 2021 • Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus
Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks.
1 code implementation • ICLR 2022 • Ge Liu, Alexander Dimitrakakis, Brandon Carter, David Gifford
We introduce the maximum $n$-times coverage problem that selects $k$ overlays to maximize the summed coverage of weighted elements, where each element must be covered at least $n$ times.
2 code implementations • NeurIPS 2021 • Brandon Carter, Siddhartha Jain, Jonas Mueller, David Gifford
Here, we demonstrate that neural networks trained on CIFAR-10 and ImageNet suffer from overinterpretation, and we find models on CIFAR-10 make confident predictions even when 95% of input images are masked and humans cannot discern salient features in the remaining pixel-subsets.
1 code implementation • 10 Dec 2019 • Angie Boggust, Brandon Carter, Arvind Satyanarayan
Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics.
1 code implementation • 9 Oct 2018 • Brandon Carter, Jonas Mueller, Siddhartha Jain, David Gifford
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model.