There is an observed average lift (with respect to both team and developer assignment) of 13%-points in 11-fold incremental-learning cross-validation (IL-CV) accuracy for Dual DNN utilizing owner-weighted labels compared with the traditional multi-class classification approach.
Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways.
Weakly-supervised instance segmentation aims to detect and segment object instances precisely, given imagelevel labels only.
In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies.
In this paper, we propose an alternating numerical scheme whose core is the sub-maximization problem in the trace-fractional form with an orthogonal constraint.
We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks.
In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias.