Perspective changes in human listeners are aligned with the contextual transformation of the word embedding space.
Sign Up to like & getrecommendations! Published in 2023 at "Cerebral cortex"
DOI: 10.1093/cercor/bhad082
Abstract: Word embedding representations have been shown to be effective in predicting human neural responses to lingual stimuli. While these representations are sensitive to the textual context, they lack the extratextual sources of context such as… read more here.
Keywords: human listeners; perspective changes; space; word embedding ... See more keywords
Contrastive Learning Over Random Fourier Features for IoT Network Intrusion Detection
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3214758
Abstract: IoT networks connect dispersed and heterogeneous devices with high economic impact, making them an important cybersecurity target. The changing nature of cybersecurity attacks requires intrusion detectors with the ability to detect new attacks. Our proposed… read more here.
Keywords: embedding space; network; network intrusion; contrastive learning ... See more keywords
Towards Effective Deep Embedding for Zero-Shot Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2020.2984666
Abstract: Zero-shot learning (ZSL) can be formulated as a cross-domain matching problem: after being projected into a joint embedding space, a visual sample will match against all candidate class-level semantic descriptions and be assigned to the… read more here.
Keywords: embedding space; shot learning; zero shot; class ... See more keywords