Search Results for author:
Found 15 papers, 2 papers with code
AnonPSI: An Anonymity Assessment Framework for PSI
We conducted a comprehensive performance evaluation of various attack strategies proposed utilizing two real datasets.
An automated approach to extracting positive and negative clinical research results
Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken.
DP-FP: Differentially Private Forward Propagation for Large Models
Our DP-FP employs novel (1) representation clipping followed by noise addition in the forward propagation stage, as well as (2) micro-batch construction via subsampling to achieve DP amplification and reduce noise power to $1/M$, where $M$ is the number of micro-batch in a step.
Distinguishing Transformative from Incremental Clinical Evidence: A Classifier of Clinical Research using Textual features from Abstracts and Citing Sentences
In this study, a machine learning approach is proposed to distinguishing transformative from incremental clinical evidence.
Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements
Design/methodology/approach: Taking cardiovascular research publications in China as a sample, we extracted the SPO triples as knowledge unit and the hedging/conflicting uncertainties as the knowledge context.
A comment-driven evidence appraisal approach for decision-making when only uncertain evidence available
Conclusions: Comment-derived evidence assertions have the potential as an evidence appraisal tool for heuristic decisions based on the accuracy, sensitivity, and efficiency of evidence-comment networks.
Dynamic Differential-Privacy Preserving SGD
The equivalent privacy costs controlled by maintaining the same gradient clipping thresholds and noise powers in each step result in unstable updates and a lower model accuracy when compared to the non-DP counterpart.
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model.
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning
Our introduced SGLD oracle would lower generalization errors in local node's parameter learning and provide local node DP protections.
Towards Medical Knowmetrics: Representing and Computing Medical Knowledge using Semantic Predications as the Knowledge Unit and the Uncertainty as the Knowledge Context
This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context.
Variational Optimization for the Submodular Maximum Coverage Problem
We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications.
Topology Adaptive Graph Convolutional Networks
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss.
Convergence analysis of belief propagation for pairwise linear Gaussian models
Gaussian belief propagation (BP) has been widely used for distributed inference in large-scale networks such as the smart grid, sensor networks, and social networks, where local measurements/observations are scattered over a wide geographical area.
Convergence analysis of the information matrix in Gaussian belief propagation
Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local measurements/observations are scattered over a wide geographical area.
Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
A necessary and sufficient convergence condition for the belief mean vector to converge to the optimal centralized estimator is provided under the assumption that the message information matrix is initialized as a positive semidefinite matrix.
