Search Results for author:
Found 13 papers, 8 papers with code
DPAUC: Differentially Private AUC Computation in Federated Learning
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants.
Differentially Private AUC Computation in Vertical Federated Learning
In this work, we propose two evaluation algorithms that can more accurately compute the widely used AUC (area under curve) metric when using label DP in vFL.
Differentially Private Label Protection in Split Learning
Split learning is a distributed training framework that allows multiple parties to jointly train a machine learning model over vertically partitioned data (partitioned by attributes).
Defending against Reconstruction Attack in Vertical Federated Learning
Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared gradient.
Vertical Federated Learning without Revealing Intersection Membership
In this paper, we propose a vFL framework based on Private Set Union (PSU) that allows each party to keep sensitive membership information to itself.
Label Leakage and Protection in Two-party Split Learning
Two-party split learning is a popular technique for learning a model across feature-partitioned data.
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating).
Bag of Freebies for Training Object Detection Neural Networks
Training heuristics greatly improve various image classification model accuracies~\cite{he2018bag}.
Bag of Tricks for Image Classification with Convolutional Neural Networks
Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods.
Ranked #38 on
Domain Generalization
on VizWiz-Classification
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
As 3D movie viewing becomes mainstream and Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly.
Unsupervised Deep Embedding for Clustering Analysis
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.
Ranked #4 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Image Denoising and Inpainting with Deep Neural Networks
Our method achieves state-of-the-art performance in the image denoising task.
