Uncertainty
Tractable Function-Space Variational Inference in Bayesian Neural Networks Tim G. J. Rudner
A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distribution over the network parameters, infer an approximate posterior distribution, and use it to make stochastic predictions.
Collaborative Causal Discovery with Atomic Interventions
Structural Causal Models (SCM) framework introduced by Pearl [2009].
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci Dept of Computer Science ETH Zürich lorenzo.noci@inf.ethz.ch Kevin Roth
The " cold posterior effect " (CPE) in Bayesian deep learning describes the uncom-forting observation that the predictive performance of Bayesian neural networks
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation
D2C uses a learned diffusion-based prior over the latent representations to improve generation and contrastive self-supervised learning to improve representation quality.
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
As a result we can learn a kernel that transfers to new tasks; we call this Deep Kernel Transfer (DKT) .
One-shot learning and big data with n=2
The proposed methodology is a variant of principal component regression (PCR).
