The article discusses the importance of heteroscedastic predictive uncertainties in Bayesian Neural Networks (BNNs). The authors illustrate how both heteroscedastic aleatoric and epistemic variance can be integrated into the variances…
Continue readingThe study proposes Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) for building uncertainty-aware self-attention in transformers. The asymmetry of attention kernels is addressed using Kernel SVD (KSVD), yielding reduced complexity….
Continue readingThe authors propose a new variational framework for performing inference in stochastic differential equations (SDEs) driven by fractional Brownian motion (fBM). SDEs are versatile tools for modeling real-world dynamic systems…
Continue readingThe article presents Neural Operator Variational Inference (NOVI) for Deep Gaussian Processes (DGP). NOVI uses a neural generator to obtain a sampler and minimizes the Regularized Stein Discrepancy between the…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
