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Found 7 papers, 4 papers with code
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths
We consider the problem of sampling transition paths between two given metastable states of a molecular system, e. g. a folded and unfolded protein or products and reactants of a chemical reaction.
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
We focus on the problem of efficient sampling and learning of probability densities by incorporating symmetries in probabilistic models.
[Re] Parameterized Explainer for Graph Neural Network
Due to numerous inconsistencies between code and paper, it is not possible to replicate the original results using the paper alone.
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.
Prototype-based Neural Network Layers: Incorporating Vector Quantization
Neural networks currently dominate the machine learning community and they do so for good reasons.
