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Found 12 papers, 3 papers with code
Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
To explore such important dependencies, we propose a novel model parsing method called Learnable Graph Pooling Network (LGPN).
Privacy-Preserving Community Detection for Locally Distributed Multiple Networks
To remove the bias incurred by RR and the squared network matrices, we develop a two-step bias-adjustment procedure.
Hierarchical Fine-Grained Image Forgery Detection and Localization
As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation.
Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks
In particular, we derive a closed-form expression for the local update step and use the iterative proximal projection method to deal with the group fused lasso penalty in the global update step.
Multi-domain Learning for Updating Face Anti-spoofing Models
In this work, we study multi-domain learning for face anti-spoofing(MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating.
FedPower: Privacy-Preserving Distributed Eigenspace Estimation
The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging.
Discourse-level Relation Extraction via Graph Pooling
The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for discourse-level relation extraction (DRE) tasks.
CORD19STS: COVID-19 Semantic Textual Similarity Dataset
To overcome this gap, we introduce CORD19STS dataset which includes 13, 710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge.
Randomized spectral co-clustering for large-scale directed networks
Directed networks are broadly used to represent asymmetric relationships among units.
Randomized Spectral Clustering in Large-Scale Stochastic Block Models
In this paper, we study the spectral clustering using randomized sketching algorithms from a statistical perspective, where we typically assume the network data are generated from a stochastic block model that is not necessarily of full rank.
Differentially Private Precision Matrix Estimation
In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information.
Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies
Skel-TNet consists of three components: SkelNet and a Recurrent Neural Network, they have advantages in learning spatial and temporal dependencies for predicting human motion, respectively; a feed-forward network that outputs the final estimation.
