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Found 5 papers, 3 papers with code
GRAND: Graph Neural Diffusion
We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.
Statistically Robust Neural Network Classification
The SRR provides a distinct and complementary measure of robust performance, compared to natural and adversarial risk.
A Statistical Approach to Assessing Neural Network Robustness
Furthermore, it provides an ability to scale to larger networks than formal verification approaches.
Faithful Inversion of Generative Models for Effective Amortized Inference
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data.
