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Found 3 papers, 1 papers with code
Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies
We show that otherwise it is possible to directly recover all vectors in a mini-batch without any numerical optimisation due to the de-mixing nature of the cross entropy loss.
Copula Flows for Synthetic Data Generation
Learning the probabilistic model for the data is equivalent to estimating the density of the data.
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks.
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