Vector Learning for Cross Domain Representations. Authors:Shagan Sah, Chi Zhang, Thang Nguyen, Dheeraj Kumar Peri, Ameya Shringi, Raymond Ptucha.

In this paper, we propose a novel learning framework for domain-transfer learning based on both instances and at- tributes. We proposed to embed the attributes ...

We propose an unsupervised cross-domain rumor detection model that enhances contrastive learning and cross-attention by label-aware learning to alleviate the ...

This paper proposes an architecture that incorporates domain knowledge into a neural sentiment classification model that provides a cross-domain model and ...

We investigate this problem by learning domain-specific representations of input sentences using neural network. In particular, a descriptor vector is learned ...

This paper conducts representation learning research for cross-domain scenarios. First, we use different network representation learning methods to perform ...

Learn- ing multi-domain representations is a challenging task and requires to leverage commonalities in the domains while minimizing interference (negative ...

Sep 27, 2024 · One involves generating the latent preferences using context vectors, rating matrices or both that identify as mapping representations of users ...

Feb 8, 2022 · This paper introduces a self-supervised learning framework suitable for hyperspectral images that are inherently challenging to annotate. The ...

We present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed ...