… To handle this challenge, we use limited labeled data along with abundantly available …
across domains. We refer to our approach as global and local graph neural network (GLEN). …

… are frequent for (un)supervised domain adaptation. [15]–[18] … in disaster domain, we propose
a Graph Neural Network based approach which explicitly models both document-level local …

… our approach as Global and Local Graph Neural Network (GLEN)… methods for UDA in the
area of disaster management. We … are frequent for (un)supervised domain adaptation. [15]–[18] …

… we hypothesize that unsupervised domain adaptation through … -based neural network
architecture by comparing it to the state-… Herndon, “Disaster response aided by tweet classification …

… a model trained with supervised learning. There is also … strategies—domain adversarial
training of neural network (… practitioners in operational emergency response settings to select a …

… learning in disaster response followed by approaches which … approaches are designed for
domain adaptation setting in English … Unsupervised Domain Adaptation with Global and Local …

… region (new domain) for supervised learning is costly and time… acyclic graph (DAG) and the
optimal network architecture … for lightweight neural networks under a limited resource budget …

… Tian, "Self-supervised visual feature learning with deep neural networks: A survey," IEEE
transactions on pattern analysis and machine intelligence, vol. 43, no. 11, pp. …

… [6] proposed a semi-supervised multi-graph joint embedding (SMGJE) model to better …
, methods such as number domain confrontation neural network (DANN) [34] and condition …

… -good reasons, eg emergency response to natural disasters like Haiti … that our model is a
hierarchical neural network with a set of … We can cite: [333] for unsupervised domain adaptation …