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Found 19 papers, 4 papers with code
Weights Shuffling for Improving DPSGD in Transformer-based Models
Differential Privacy (DP) mechanisms, especially in high-dimensional settings, often face the challenge of maintaining privacy without compromising the data utility.
Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled.
Hufu: A Modality-Agnositc Watermarking System for Pre-Trained Transformers via Permutation Equivariance
The permutation equivariance ensures minimal interference between these two sets of model weights and thus high fidelity on downstream tasks.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models
However, such methods can not defend against adaptive attacks, in which an attacker takes a countermove against a known defence strategy.
Crossword: A Semantic Approach to Data Compression via Masking
The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i. i. d.
Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud
Experimental results show that our system not only adapts well to, but also draws on the varying contexts, delivering a practical and efficient solution to edge-cloud model training.
Ranked #2 on
Recommendation Systems
on MovieLens 1M
(Precision metric)
Privacy Threats Analysis to Secure Federated Learning
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties.
Improved Matrix Gaussian Mechanism for Differential Privacy
The wide deployment of machine learning in recent years gives rise to a great demand for large-scale and high-dimensional data, for which the privacy raises serious concern.
Privacy-Preserving Federated Learning on Partitioned Attributes
Real-world data is usually segmented by attributes and distributed across different parties.
Certified Distributional Robustness via Smoothed Classifiers
We focus on certified robustness of smoothed classifiers in this work, and propose to use the worst-case population loss over noisy inputs as a robustness metric.
Context-aware deep model compression for edge cloud computing
While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones.
Ranked #176 on
Image Classification
on CIFAR-10
Certified Distributional Robustness on Smoothed Classifiers
For smoothed classifiers, we propose the worst-case adversarial loss over input distributions as a robustness certificate.
High-Order Relation Construction and Mining for Graph Matching
Iterated line graphs are introduced for the first time to describe such high-order information, based on which we present a new graph matching method, called High-order Graph Matching Network (HGMN), to learn not only the local structural correspondence, but also the hyperedge relations across graphs.
Achieving Adversarial Robustness via Sparsity
Through experiments on a variety of adversarial pruning methods, we find that weights sparsity will not hurt but improve robustness, where both weights inheritance from the lottery ticket and adversarial training improve model robustness in network pruning.
Rotation-Equivariant Neural Networks for Privacy Protection
Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number.
Deep Quaternion Features for Privacy Protection
We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information.
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud
Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market.
Interpretable Complex-Valued Neural Networks for Privacy Protection
Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features.
