Search Results for author:
Found 7 papers, 4 papers with code
DeepROCK: Error-controlled interaction detection in deep neural networks
The complexity of deep neural networks (DNNs) makes them powerful but also makes them challenging to interpret, hindering their applicability in error-intolerant domains.
Sequence-to-sequence translation from mass spectra to peptides with a transformer model
A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum.
DANCE: Enhancing saliency maps using decoys
Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier.
Robust saliency maps with distribution-preserving decoys
In this work, we propose a data-driven technique that uses the distribution-preserving decoys to infer robust saliency scores in conjunction with a pre-trained convolutional neural network classifier and any off-the-shelf saliency method.
apricot: Submodular selection for data summarization in Python
This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets.
Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions
We study the problem of maximizing deep submodular functions (DSFs) subject to a matroid constraint.
DeepPINK: reproducible feature selection in deep neural networks
In this paper, we describe a method to increase the interpretability and reproducibility of DNNs by incorporating the idea of feature selection with controlled error rate.
