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Found 9 papers, 0 papers with code
Neural Node Matching for Multi-Target Cross Domain Recommendation
To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i. e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions.
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes.
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
In this paper, we propose a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously.
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors.
Audio Injection Adversarial Example Attack
We study the problem of audio adversarial example attacks with sparse perturbations.
THOR, Trace-based Hardware-adaptive layer-ORiented Natural Gradient Descent Computation
It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.
Adversarial Samples on Android Malware Detection Systems for IoT Systems
An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis.
Weighted-Sampling Audio Adversarial Example Attack
In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack.
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
Our experimental results show that both the MNIST images and the CIFAR-10 images can be perturbed to successful generate a black-box attack with 100\% probability on average.
