Search Results for author:
Found 5 papers, 5 papers with code
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks.
Maximum n-times Coverage for Vaccine Design
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.
Overinterpretation reveals image classification model pathologies
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.
Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
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.
What made you do this? Understanding black-box decisions with sufficient input subsets
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model.
