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Found 9 papers, 0 papers with code
LocalGCL: Local-aware Contrastive Learning for Graphs
Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings.
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning
In response to legislation mandating companies to honor the \textit{right to be forgotten} by erasing user data, it has become imperative to enable data removal in Vertical Federated Learning (VFL) where multiple parties provide private features for model training.
MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems
Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly.
Temporal Gradient Inversion Attacks with Robust Optimization
We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients.
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
To overcome these limitations, we propose a novel adaptive mechanism for cross-silo FL, towards incentivizing organizations to contribute data to maximize their long-term payoffs in a real dynamic training environment.
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations.
Association: Remind Your GAN not to Forget
Neural networks are susceptible to catastrophic forgetting.
Generative and Discriminative Learning for Distorted Image Restoration
Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task.
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks
Random selection based defenses can achieve certified robustness by averaging the classifiers' predictions on the sub-datasets sampled from the training set.
