Search Results for author:
Found 9 papers, 7 papers with code
Differentially Private Federated Learning with Local Regularization and Sparsification
User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning.
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Showing how vision Transformers are at the risk of privacy leakage via gradients, we urge the significance of designing privacy-safer Transformer models and defending schemes.
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy
In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS.
Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices
In this paper, we present a unified framework with Joint Channel pruning and Weight pruning (JCW), and achieves a better Pareto-frontier between the latency and accuracy than previous model compression approaches.
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records
In this paper, we propose MetaPred, a meta-learning for clinical risk prediction from longitudinal patient EHRs.
Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Sub-phenotype III is with average age 65. 07$ \pm 11. 32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1. 69\pm 0. 32$ mg/dL, eGFR $93. 97\pm 56. 53$ mL/min/1. 73$m^2$).
Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network
For complicated diseases such as Parkinson's and Alzheimer's, both EHR and neuroimaging information are very important for disease understanding because they contain complementary aspects of the disease.
A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations
This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations.
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.
