Search Results for author:
Found 6 papers, 1 papers with code
Adiabatic Quantum Support Vector Machines
Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with increasing number of features and number of data points in the training dataset.
Topological Interpretability for Deep-Learning
We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods.
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping
Word embeddings that effectively capture the meaning and context of the word that they represent can significantly improve the performance of downstream DL models for various NLP tasks.
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest.
Hierarchical Convolutional Attention Networks for Text Classification
Recent work in machine translation has demonstrated that self-attention mechanisms can be used in place of recurrent neural networks to increase training speed without sacrificing model accuracy.
Precision Medicine as an Accelerator for Next Generation Cognitive Supercomputing
In the past several years, we have taken advantage of a number of opportunities to advance the intersection of next generation high-performance computing AI and big data technologies through partnerships in precision medicine.
