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2020 Conference Publication Implicit surface representations as layers in neural networksMichalkiewicz, Mateusz, Pontes, Jhony Kaesemodel, Jack, Dominic, Baktashmotlagh, Mahsa and Eriksson, Anders (2020). Implicit surface representations as layers in neural networks. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October -2 November, 2019 . Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICCV.2019.00484 |
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2020 Conference Publication Progressive graph learning for open-set domain adaptationLuo, Yadan, Wang, Zijian, Huang, Zi and Baktashmotlagh, Mahsa (2020). Progressive graph learning for open-set domain adaptation. 37th International Conference on Machine Learning ICML 2020, Vienna, Austria, 12-18 July 2020 . International Machine Learning Society . |
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2019 Journal Article Visualizing student opinion through text analysisCunningham-Nelson, Samuel, Baktashmotlagh, Mahsa and Boles, Wageeh (2019). Visualizing student opinion through text analysis. IEEE Transactions on Education, 62 (4) 8759085, 305-311. doi: 10.1109/TE.2019.2924385 |
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2019 Conference Publication Learning factorized representations for open-set domain adaptationBaktashmotlagh, Mahsa, Faraki, Masoud, Drummond, Tom and Salzmann, Mathieu (2019). Learning factorized representations for open-set domain adaptation. 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, United States, 6 - 9 May 2019. International Conference on Learning Representations, ICLR. |
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2019 Conference Publication Object graph networks for spatial language groundingHawkins, Philip, Maire, Frederic, Denman, Simon and Baktashmotlagh, Mahsa (2019). Object graph networks for spatial language grounding. APRS International Conference on Digital Image Computing - Techniques and Applications (DICTA), Perth, Australia, 2-4 December 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA47822.2019.8946101 |
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2019 Conference Publication Multi-Component Image Translation for Deep Domain GeneralizationRahman, Mohammad Mahfujur, Fookes, Clinton, Baktashmotlagh, Mahsa and Sridharan, Sridha (2019). Multi-Component Image Translation for Deep Domain Generalization. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI United States, 7-11 January 2019. Piscataway, NJ United States: IEEE. doi: 10.1109/wacv.2019.00067 |
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2017 Conference Publication Speaker verification with multi-run ICA based speech enhancementAl-Ali, Ahmed Kamil Hasan, Dean, David, Senadji, Bouchra, Baktashmotlagh, Mahsa and Chandran, Vinod (2017). Speaker verification with multi-run ICA based speech enhancement. 2017 11th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, QLD Australia, 13-15 December 2017. Piscataway, NJ United States: IEEE. doi: 10.1109/icspcs.2017.8270505 |
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2017 Conference Publication Deep discovery of facial motions using a shallow embedding layerGhasemi, Afsaneh, Baktashmotlagh, Mahsa, Denman, Simon, Sridharan, Sridha, Tien, Dung Nguyen and Fookes, Clinton (2017). Deep discovery of facial motions using a shallow embedding layer. 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 17-20 September 2017. Piscataway, NJ, United States: IEEE. doi: 10.1109/icip.2017.8296545 |
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2017 Conference Publication From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification ApproachErfani, Sarah, Baktashmotlagh, Mahsa, Moshtaghi, Masud, Nguyen, Vinh, Leckie, Christopher, Bailey, James and Ramamohanarao, Kotagiri (2017). From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach. Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA United States, 4-9 February 2017. Palo Alto, CA United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v31i1.10870 |
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2017 Book Chapter Learning Domain Invariant Embeddings by Matching DistributionsBaktashmotlagh, Mahsa, Harandi, Mehrtash and Salzmann, Mathieu (2017). Learning Domain Invariant Embeddings by Matching Distributions. Domain Adaptation in Computer Vision Applications. (pp. 95-114) Cham, Switzerland: Springer. doi: 10.1007/978-3-319-58347-1_5 |
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2016 Journal Article Distribution-matching embedding for visual domain adaptationBaktashmotlagh, Mahsa, Harandi, Mehrtash and Salzmann, Mathieu (2016). Distribution-matching embedding for visual domain adaptation. Journal of Machine Learning Research, 17 108, 1-30. |
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2016 Conference Publication R1STM: One-class support tensor machine with randomised kernelErfani, Sarah M., Baktashmotlagh, Mahsa, Rajasegarad, Sutharshan, Nguyen, Vinh, Leckie, Christopher, Bailey, James and Ramamohanarao, Kotagiri (2016). R1STM: One-class support tensor machine with randomised kernel. 2016 SIAM International Conference on Data Mining (SDM), Miami, FL United States, 5-7 May 2016. Philadelphia, PA United States: Society for Industrial and Applied Mathematics. doi: 10.1137/1.9781611974348.23 |
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2016 Conference Publication Robust domain generalisation by enforcing distribution invarianceErfani, Sarah M., Baktashmotlagh, Mahsa, Moshtaghi, Masud, Nguyen, Vinh, Leckie, Christopher, Bailey, James and Ramamohanarao, Kotagiri (2016). Robust domain generalisation by enforcing distribution invariance. 25th International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, United States, 9-15 July 2016. Palo Alto, CA United States: AAAI Press / International Joint Conferences on Artificial Intelligence. |
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2016 Journal Article Structure-Activity Studies of Cysteine-Rich α-Conotoxins that Inhibit High Voltage-Activated Calcium Channels via GABAB Receptor Activation Reveal a Minimal Functional MotifCarstens, Bodil B., Berecki, Geza, Daniel, James T., Lee, Han Siean, Jackson, Kathryn A. V., Tae, Han-Shen, Sadeghi, Mahsa, Castro, Joel, O'Donnell, Tracy, Deiteren, Annemie, Brierley, Stuart M., Craik, David J., Adams, David J. and Clark, Richard J. (2016). Structure-Activity Studies of Cysteine-Rich α-Conotoxins that Inhibit High Voltage-Activated Calcium Channels via GABAB Receptor Activation Reveal a Minimal Functional Motif. Angewandte Chemie - International Edition, 55 (15), 4692-4696. doi: 10.1002/anie.201600297 |
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2015 Conference Publication Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFsHarandi, Mehrtash, Salzmann, Mathieu and Baktashmotlagh, Mahsa (2015). Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 December 2015. Piscataway, NJ United States: IEEE. doi: 10.1109/iccv.2015.468 |
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2015 Conference Publication R1SVM: A randomised nonlinear approach to large-scale anomaly detectionErfani, Sarah M., Baktashmotlagh, Mahsa, Rajasegarar, Sutharshan, Karunasekera, Shanika and Leckie, Chris (2015). R1SVM: A randomised nonlinear approach to large-scale anomaly detection. AI Access Foundation. |
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2015 Conference Publication R1SVM: A randomised nonlinear approach to large-scale anomaly detectionM. Erfani, Sarah, Baktashmotlagh, Mahsa, Rajasegarar, Sutharshan, Karunasekera, Shanika and Leckie, Chris (2015). R1SVM: A randomised nonlinear approach to large-scale anomaly detection. Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, United States, 25-30 January 2015. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v29i1.9208 |
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2014 Journal Article Discriminative non-linear stationary subspace analysis for video classificationBaktashmotlagh, Mahsa, Harandi, Mehrtash, Lovell, Brian C. and Salzmann, Mathieu (2014). Discriminative non-linear stationary subspace analysis for video classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (12) 6857376, 2353-2366. doi: 10.1109/TPAMI.2014.2339851 |
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2014 Conference Publication Domain adaptation on the statistical manifoldBaktashmotlagh, Mahsa, Harandi, Mehrtash T., Lovell, Brian C. and Salzmann, Mathieu (2014). Domain adaptation on the statistical manifold. 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, United States, 23-28 June 2014. Piscataway, NJ, United States: I E E E Computer Society. doi: 10.1109/CVPR.2014.318 |
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2014 Other Outputs Learning Invariances for High-Dimensional Data AnalysisBaktashmotlagh, Mahsa (2014). Learning Invariances for High-Dimensional Data Analysis. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland. doi: 10.14264/uql.2014.183 |
