Search Results for author: William Stafford Noble

Found 7 papers, 4 papers with code

DeepROCK: Error-controlled interaction detection in deep neural networks

no code implementations26 Sep 2023 Winston Chen, William Stafford Noble, Yang Young Lu

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

2 code implementations bioRxiv 2023 Melih Yilmaz, William E. Fondrie, Wout Bittremieux, Rowan Nelson, Varun Ananth, Sewoong Oh, William Stafford Noble

A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum.

de novo peptide sequencing

DANCE: Enhancing saliency maps using decoys

1 code implementation3 Feb 2020 Yang Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble

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.

Adversarial Attack

Robust saliency maps with distribution-preserving decoys

no code implementations25 Sep 2019 Yang Young Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble

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.

Adversarial Attack

apricot: Submodular selection for data summarization in Python

1 code implementation8 Jun 2019 Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble

This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets.

Data Summarization

DeepPINK: reproducible feature selection in deep neural networks

1 code implementation NeurIPS 2018 Yang Young Lu, Yingying Fan, Jinchi Lv, William Stafford Noble

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.

feature selection

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