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Found 7 papers, 5 papers with code
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
The global inducing point variational approximation for BNNs is based on using a set of inducing inputs to construct a series of conditional distributions that accurately approximate the conditionals of the true posterior distribution.
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data.
Differentially private partitioned variational inference
In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects.
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.
Do Concept Bottleneck Models Learn as Intended?
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets.
Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors.
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering.
