Search Results for author:
Found 11 papers, 4 papers with code
Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning
In this paper, we propose a novel lossless privacy-preserving aggregation rule named LPPA to enhance gradient protection as much as possible but without loss of DFL model predictive accuracy.
A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal Adapter
With these designs, Wander enables token-level interactions between sequences of different modalities in a parameter-efficient way.
Bridging the Gap for Test-Time Multimodal Sentiment Analysis
However, in real-world dynamic scenarios, the distribution of target data is always changing and different from the source data used to train the model, which leads to performance degradation.
SEGAN: semi-supervised learning approach for missing data imputation
In addition, the SE-GAN model introduces a missing hint matrix to allow the discriminator to more effectively distinguish between known data and data filled by the generator.
Differentiable and Scalable Generative Adversarial Models for Data Imputation
DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model.
Generative Semi-supervised Learning for Multivariate Time Series Imputation
In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data.
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
In particular, we use a bipartite network to construct the user-item network, and represent the interactions among users (or items) by the corresponding one-mode projection network.
Can Adversarial Network Attack be Defended?
In this paper, we are interested in the possibility of defense against adversarial attack on network, and propose defense strategies for GNNs against attacks.
Social and Information Networks Physics and Society
GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction
To the best of our knowledge, it is the first time that GCN embedded LSTM is put forward for link prediction of dynamic networks.
Social and Information Networks Physics and Society
Link Prediction Adversarial Attack
Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification and link prediction.
Physics and Society Social and Information Networks
Fast Gradient Attack on Network Embedding
Network embedding maps a network into a low-dimensional Euclidean space, and thus facilitate many network analysis tasks, such as node classification, link prediction and community detection etc, by utilizing machine learning methods.
Physics and Society Social and Information Networks
