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Found 5 papers, 2 papers with code
Do Counterfactual Examples Complicate Adversarial Training?
We leverage diffusion models to study the robustness-performance tradeoff of robust classifiers.
Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models
The problem of pre-training data detection for large language models (LLMs) has received growing attention due to its implications in critical issues like copyright violation and test data contamination.
Adversarial Estimation of Topological Dimension with Harmonic Score Maps
Quantification of the number of variables needed to locally explain complex data is often the first step to better understanding it.
Disentangling Learning Representations with Density Estimation
Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues.
NashAE: Disentangling Representations through Adversarial Covariance Minimization
We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e. g., no assumptions on the number or distribution of the individual latent variables to be extracted).
