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Found 6 papers, 3 papers with code
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
Current certification methods are computationally expensive and limited to attacks that optimize the manipulation with respect to a norm.
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components
The decomposition of objects into generic components combined with the probabilistic reasoning provides by design a clear interpretation of the classification decision process.
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks
The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods.
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison
An appropriate choice of the activation function (like ReLU, sigmoid or swish) plays an important role in the performance of (deep) multilayer perceptrons (MLP) for classification and regression learning.
Prototype-based Neural Network Layers: Incorporating Vector Quantization
Neural networks currently dominate the machine learning community and they do so for good reasons.
Regularization in Relevance Learning Vector Quantization Using l one Norms
We propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles.
