Yij. Assume now we have a graph embeddingZ: V Rd

We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE.

Inthis paper,we address several inadequacies ofcurrent video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semisupervised process to produce more robust representations.

Ensembles of geophysical models improve prediction accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotemporally varying model weights and bias, while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertaintyaware predictions without sacrificing interpretability.

In this paper, we focus on a specific federated learning scenario: Vertical Federated Learning(VFL) [5].

CascadeXML significantly outperforms allexisting approaches with non-trivial gains obtained on benchmark datasets consisting of up to three millionlabels.

Whether when planning our paths home from the office or from a hotel to an airport in an unfamiliar city, we typically focus on a small subset of relevant variables,e.g. the changeinposition orthepresence oftraffic.

Most previous studies focus on the independent cascade (IC) model under the edge-level feedback.