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Found 4 papers, 2 papers with code
Predicting the generalization gap in neural networks using topological data analysis
Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models.
Computing the Testing Error Without a Testing Set
Here, we derive an algorithm to estimate the performance gap between training and testing without the need of a testing dataset.
What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks?
Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks.
Deep Structure Inference Network for Facial Action Unit Recognition
In recent years, most efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between Action Units.
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