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06/28/2023
VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing
Vertical Federated Learning (VFL) has emerged as one of the most predomi...
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04/04/2023
A Survey on Vertical Federated Learning: From a Layered Perspective
Vertical federated learning (VFL) is a promising category of federated l...
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06/28/2022
Secure Forward Aggregation for Vertical Federated Neural Networks
Vertical federated learning (VFL) is attracting much attention because i...
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06/30/2021
CityNet: A Multi-city Multi-modal Dataset for Smart City Applications
Data-driven approaches have been applied to many problems in urban compu...
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04/07/2021
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
Graph neural networks (GNNs) have achieved tremendous success in graph m...
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03/16/2021
Ternary Hashing
This paper proposes a novel ternary hash encoding for learning to hash m...
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11/27/2020
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Deep neural networks (DNNs) are known to be prone to adversarial attacks...
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09/24/2020
EPNE: Evolutionary Pattern Preserving Network Embedding
Information networks are ubiquitous and are ideal for modeling relationa...
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06/25/2020
Graph Structural-topic Neural Network
Graph Convolutional Networks (GCNs) achieved tremendous success by effec...
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02/26/2020
A Survey towards Federated Semi-supervised Learning
The success of Artificial Intelligence (AI) should be largely attributed...
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11/18/2019
GraLSP: Graph Neural Networks with Local Structural Patterns
It is not until recently that graph neural networks (GNNs) are adopted t...
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06/03/2019
DANE: Domain Adaptive Network Embedding
Recent works reveal that network embedding techniques enable many machin...
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01/25/2019
SecureBoost: A Lossless Federated Learning Framework
The protection of user privacy is an important concern in machine learni...
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01/04/2018