Search Results for author:
Found 14 papers, 5 papers with code
Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities
To address the problem, we propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and transfers the graph structures and forecasting models across cities.
A Survey on Vertical Federated Learning: From a Layered Perspective
From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL.
Federated Learning without Full Labels: A Survey
Therefore, the problem of federated learning without full labels is important in real-world FL applications.
Secure Forward Aggregation for Vertical Federated Neural Networks
In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
However, the inability of GNNs to model substructures in graphs remains a significant drawback.
Ternary Hashing
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed.
EPNE: Evolutionary Pattern Preserving Network Embedding
In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes.
Graph Structural-topic Neural Network
Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently.
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner.
GraLSP: Graph Neural Networks with Local Structural Patterns
Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns.
DANE: Domain Adaptive Network Embedding
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.
SecureBoost: A Lossless Federated Learning Framework
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
ICFVR 2017: 3rd International Competition on Finger Vein Recognition
The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets.
