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Found 9 papers, 4 papers with code
Improving Trainability of Variational Quantum Circuits via Regularization Strategies
In the era of noisy intermediate-scale quantum (NISQ), variational quantum circuits (VQCs) have been widely applied in various domains, advancing the superiority of quantum circuits against classic models.
Debiasing Machine Unlearning with Counterfactual Examples
This bias emerges from two main sources: (1) data-level bias, characterized by uneven data removal, and (2) algorithm-level bias, which leads to the contamination of the remaining dataset, thereby degrading model accuracy.
Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning
As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models.
Robust Node Representation Learning via Graph Variational Diffusion Networks
To learn robust node representation in the presence of perturbations, various works have been proposed to safeguard GNNs.
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation
In this work, we propose a new label inference model, namely LInDT, which integrates both Bayesian label transition and topology-based label propagation for improving the robustness of GNNs against topological perturbations.
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision
In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task.
Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks
Our proposed model synthesizes Gaussian mixture based latent representation over a deep generative model, such as GAN, for incremental detection of instances of emerging classes in the test data.
Deperturbation of Online Social Networks via Bayesian Label Transition
However, a small number of users, so-called perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task.
Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks
To the best of our knowledge, BANIS is the first application to synthesize microscopic images that associate distinct spatial geometric features from multi-source domains.
