461106. Semi- and unsupervised learning
| Title | 461106. Semi- and unsupervised learning |
|---|---|
| Parent | 4611. Machine learning |
Latest research outputs
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Guided Disentangled Representation Learning from Audio data for Transfer Learning
Haque, Kazi Nazmul. 2024. Guided Disentangled Representation Learning from Audio data for Transfer Learning. PhD by Publication Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/z9y75PhD by Publication
Artificial intelligence informed simulation of dissolved Inorganic Nitrogen from ungauged catchments to the Great Barrier Reef
O’Sullivan, Cherie. 2023. Artificial intelligence informed simulation of dissolved Inorganic Nitrogen from ungauged catchments to the Great Barrier Reef. PhD by Publication Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/z6206PhD by Publication
Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja and Schuller, Bjorn W.. 2023. "Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition ." IEEE Transactions on Affective Computing. 14 (4), pp. 3164-3176. https://doi.org/10.1109/TAFFC.2022.3221749Article
Sleep stage classification in EEG signals using the clustering approach based probability distribution features coupled with classification algorithms
Al-Salman, Wessam, Li, Yan, Oudah, Atheer Y. and Almaged, Sadiq. 2023. "Sleep stage classification in EEG signals using the clustering approach based probability distribution features coupled with classification algorithms." Neuroscience Research. https://doi.org/10.1016/j.neures.2022.09.009Article
Multimodality Information Fusion for Automated Machine Translation
Li, Lin, Tayir, Turghun, Han, Yifeng, Tao, Xiaohui and Velasquez, Juan D.. 2023. "Multimodality Information Fusion for Automated Machine Translation." Information Fusion. 91, pp. 352-363. https://doi.org/10.1016/j.inffus.2022.10.018Article
Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model
Ahmed, Abul Abrar Masrur, Deo, Ravinesh C., Ghimire, Sujan, Downs, Nathan J., Devi, Aruna, Barua, Prabal D. and Yaseen, Zaher M.. 2022. "Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model." Sustainability. 14 (17), pp. 1-27. https://doi.org/10.3390/su141711070Article
Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing
Ali, Hafiz Shehbaz, Hassan, Fakhar ul, Latif, Siddique, Manzoor, Habib Ullah and Qadir, Junaid. 2021. "Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing." IEEE International Conference on Communications Workshops (2021). Montreal, Canada 14 - 23 Jun 2021 United States. https://doi.org/10.1109/ICCWorkshops50388.2021.9473669Paper
Extracting epileptic features in EEGs using a dual-tree complex wavelet transform coupled with a classification algorithm
Al-Salman, Wessam, Li, Yan, Wen, Peng, Miften, Firas Sabar, Oudah, Atheer Y. and Ghayab, Hadi Ratham Al. 2022. "Extracting epileptic features in EEGs using a dual-tree complex wavelet transform coupled with a classification algorithm." Brain Research. 1779. https://doi.org/10.1016/j.brainres.2022.147777Article
Deep Representation Learning for Speech Emotion Recognition
Latif, Siddique. 2022. Deep Representation Learning for Speech Emotion Recognition. PhD by Publication Doctor of Philosophy (DPHD). University of Southern Queensland. https://doi.org/10.26192/w8w00PhD by Publication
Survey of Deep Representation Learning for Speech Emotion Recognition
Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja, Qadir, Junaid and Schuller, Bjorn. 2023. "Survey of Deep Representation Learning for Speech Emotion Recognition." IEEE Transactions on Affective Computing. 14 (2), pp. 1634-1654. https://doi.org/10.1109/TAFFC.2021.3114365Article
Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier
Al-Salman, Wessam, Li, Yan and Wen, Peng. 2021. "Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier." Neuroscience Research. 172, pp. 26-40. https://doi.org/10.1016/j.neures.2021.03.012Article
Variational Autoencoders to Learn Latent Representations of Speech Emotion
Latif, Siddique, Rana, Rajib, Qadir, Junaid and Epps, Julien. 2018. "Variational Autoencoders to Learn Latent Representations of Speech Emotion." 19th Annual Conference of the International Speech Communication Association: Speech Research for Emerging Markets in Multilingual Societies (INTERSPEECH 2018). Hyderabad, India 02 - 06 Sep 2018 France. https://doi.org/10.21437/Interspeech.2018-1568Paper