In recent years, a large number of algorithms for label integration and noise correction have been proposed to infer the unknown true labels of instances in crowdsourcing.

Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting

In this paper, we introduce a new type of model backdoor: the privacy backdoor attack .

We present novel graph kernels for graphs with node and edge labels that have ordered neighborhoods, i.e. when neighbor nodes followanorder.

In this work we target a learnableoutputrepresentation that allows continuous, high resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a Neural Network, thereby breaking previous barriers in resolution, and ability to represent diverse topologies. However, neural implicit representations arelimited to closed surfaces, which divide the space into inside and outside. Many real world objects such as walls of a scene scannedby a sensor,clothing,or a car with innerstructuresare not closed. Thisconstitutesa significant barrier,in termsof datapre-processing (objects need to be artificially closed creating artifacts), and the ability to output open surfaces. In this work, we proposeNeural Distance Fields (NDF), a neural network based model which predicts the unsigned distance field for arbitrary 3D shapes given sparse point clouds. NDF represent surfaces at high resolutions as prior implicit models, but do not require closed surface data, and significantly broaden the class of representable shapes in the output.