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Found 7 papers, 1 papers with code
Particle physics DL-simulation with control over generated data properties
The research of innovative methods aimed at reducing costs and shortening the time needed for simulation, going beyond conventional approaches based on Monte Carlo methods, has been sparked by the development of collision simulations at the Large Hadron Collider at CERN.
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures.
Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN
In particular, we examine the performance of variational autoencoders and generative adversarial networks, expanding the GAN architecture by an additional regularisation network and a simple, yet effective postprocessing step.
Towards More Realistic Membership Inference Attacks on Large Diffusion Models
In this paper, we examine whether it is possible to determine if a specific image was used in the training set, a problem known in the cybersecurity community and referred to as a membership inference attack.
Progressive Latent Replay for efficient Generative Rehearsal
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network.
Selectively increasing the diversity of GAN-generated samples
Especially prone to mode collapse are conditional GANs, which tend to ignore the input noise vector and focus on the conditional information.
End-to-end Sinkhorn Autoencoder with Noise Generator
To address these shortcomings, we introduce a novel method dubbed end-to-end Sinkhorn Autoencoder, that leverages sinkhorn algorithm to explicitly align distribution of encoded real data examples and generated noise.
