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Found 5 papers, 1 papers with code
CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation
We develop the CloudEval-YAML benchmark with practicality in mind: the dataset consists of hand-written problems with unit tests targeting practical scenarios.
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation
In this paper, we recognize that images share common features in a hierarchical way due to the inherent hierarchical structure of the classification system, which is overlooked by current data parameterization methods.
Robust Trajectory Prediction against Adversarial Attacks
We demonstrate that our method is able to improve the performance by 46% on adversarial data and at the cost of only 3% performance degradation on clean data, compared to the model trained with clean data.
Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion
Our work is the first to provide a thorough understanding of leveraging model inversion for effective backdoor removal by addressing key questions about reconstructed samples' properties, perceptual similarity, and the potential presence of backdoor triggers.
On The Adversarial Robustness of 3D Point Cloud Classification
Since adversarial training (AT) is believed to be the most effective defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the model's robustness under AT.
