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Found 2 papers, 1 papers with code
Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold
This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train.
Detecting out-of-distribution text using topological features of transformer-based language models
We attempt to detect out-of-distribution (OOD) text samples though applying Topological Data Analysis (TDA) to attention maps in transformer-based language models.
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