Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem.
In this paper, we separately model the homogenous structural relationship by a modality-specific graph within individual modality and then mine the heterogeneous structural correlation in these two modality-specific graphs.
Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic.
To motivate a wide investigation in such settings, we present a real-world fine-grained domain adaptation task in machine translation (FDMT).
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance.
Turing test was originally proposed to examine whether machine's behavior is indistinguishable from a human.
This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change.
SLRM model takes advantage of the nuclear norm regularization on mapping to effectively capture the label correlations.
Categorization axioms have been proposed to axiomatizing clustering results, which offers a hint of bridging the difference between human recognition system and machine learning through an intuitive observation: an object should be assigned to its most similar category.