103 citations · 238 across the 9 of their papers we have counts for
9 papers
Global explainability of a deep abstaining classifier
Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Benjamin H. McMahon +8
We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based…
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis
Alexander Partin, Priyanka Vasanthakumari, Oleksandr Narykov +17
Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question,…
Hybrid programming-model strategies for GPU offloading of electronic structure calculation kernels
Jean-Luc Fattebert, Christian F. A. Negre, Joshua Finkelstein +6
To address the challenge of performance portability, and facilitate the implementation of electronic structure solvers, we developed the Basic Matrix Library (BML) and Parallel, Ra…
Enabling particle applications for exascale computing platforms
Susan M Mniszewski, James Belak, Jean-Luc Fattebert +21
The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applic…
Performance Optimizations of Recursive Electronic Structure Solvers targeting Multi-Core Architectures (LA-UR-20-26665)
Adetokunbo A. Adedoyin, Christian F. A. Negre, Jamaludin Mohd-Yusof +6
As we rapidly approach the frontiers of ultra large computing resources, software optimization is becoming of paramount interest to scientific application developers interested in…
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Kumkum Ganguly +13
Safe deployment of deep learning systems in critical real world applications requires models to make very few mistakes, and only under predictable circumstances. In this work, we a…
Combating Label Noise in Deep Learning Using Abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes +2
We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby…
Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness
Nicholas Lubbers, David C. Bolton, Jamaludin Mohd-Yusof +3
Machine learning regression can predict macroscopic fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions.…
Graph-based linear scaling electronic structure theory
Anders M. N. Niklasson, Susan M. Mniszewski, Christian F. A. Negre +8
We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broa…