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GLoMo: Unsupervised Learning of Transferable Relational Graphs
2018
Neural Information Processing Systems
Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g.,
dblp:conf/nips/YangZDHCSL18
fatcat:4pviuw5guzgg7lu5i7y2llstei