Modern deep learning, while effective in tackling clean and curated datasets, is often very brittle to domain and distribution shifts [76].

We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence.

While DenseNet is a typical example of the layer aggregation mechanism, its redundancyhas been commonly criticized inthe literature.

Additionally,bGPFA uses automatic relevance determination to infer the dimensionality of neural activity directly from the training data during optimization.