Dr Ehsan Abbasnejad
Senior Lecturer
Australian Institute for Machine Learning - Operations
Division of Research and Innovation
Eligible to supervise Masters and PhD, but is currently at capacity - email supervisor to discuss availability.
My research focuses on various aspects of machine learning and computer vision. For more details see my home page: https://ehsanabb.github.io/
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Appointments
Date Position Institution name 2022 - ongoing Senior Lecturer University of Adelaide 2022 - ongoing Future Making Fellow Australian Institute for Machine Learning (AIML) -
Certifications
Date Title Institution name Country 2015 PhD Australian National University Australia -
Research Interests
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Journals
Year Citation 2022 Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2022). Show, price and negotiate: a hierarchical attention recurrent visual negotiator. IEEE Transactions on Multimedia, 24, 1426-1434.
2021 Ghassemzadeh, S., Gonzalez Perdomo, M., Haghighi, M., & Abbasnejad, M. (2021). A data-driven reservoir simulation for natural gas reservoirs. Neural Computing and Applications, 33(18), 11777-11798.
Scopus4 WoS32021 Neshat, M., Nezhad, M. M., Abbasnejad, E., Mirjalili, S., Tjernberg, L. B., Astiaso Garcia, D., . . . Wagner, M. (2021). A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm. Energy Conversion and Management, 236, 1-25.
Scopus39 WoS382021 Neshat, M., Nezhad, M. M., Abbasnejad, E., Mirjalili, S., Groppi, D., Heydari, A., . . . Wagner, M. (2021). Wind turbine power output prediction using a new hybrid neuro-evolutionary method. Energy, 229, 120617-1-120617-24.
Scopus31 WoS242020 Abbasnejad, M. E., Shi, Q., van den Hengel, A., & Liu, L. (2020). GADE: A Generative Adversarial Approach to Density Estimation and its Applications. International Journal of Computer Vision, 128(10-11), 2731-2743.
Scopus12019 Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation.. CoRR, abs/1907.03076. 2017 Khoshkbarforoushha, A., Ranjan, R., Gaire, R., Abbasnejad, E., Wang, L., & Zomaya, A. Y. (2017). Distribution Based Workload Modelling of Continuous Queries in Clouds. IEEE Transactions on Emerging Topics in Computing, 5(1), 120-133.
Scopus332017 Abbasnejad, M., Shi, Q., Abbasnejad, I., Hengel, A., & Dick, A. (2017). Bayesian Conditional Generative Adverserial Networks.. CoRR, abs/1706.05477. 2013 Sadeghi, A., Ismail, A., Ahmad, A., & Abbasnejad, M. (2013). A note on solving the fuzzy Sylvester matrix equation. Journal of Computational Analysis and Applications, 15(1), 10-22.
Scopus1 WoS12013 Halin, A. A., Rajeswari, M., & Abbasnejad, M. (2013). Soccer event detection via collaborative multimodal feature analysis and candidate ranking. International Arab Journal of Information Technology, 10(5).
Scopus152012 Abbasnejad, M., Ramachandram, D., & Mandava, R. (2012). A survey of the state of the art in learning the kernels. Knowledge and Information Systems, 31(2), 193-221.
Scopus33 WoS282011 Abbasnejad, M., Ramachandram, D., & Mandava, R. (2011). An unsupervised approach to learn the kernel functions: From global influence to local similarity. Neural Computing and Applications, 20(5), 703-715.
Scopus2 WoS22011 Sadeghi, A., Abbasbandy, S., & Abbasnejad, M. (2011). The common solution of the pair of fuzzy matrix equations. World Applied Sciences Journal, 15(2), 232-238.
Scopus42011 Abbasnejad, M., Halin, A., Manshor, N., & Rajeswari, M. (2011). Automatic image annotation using mixtures of the exponential family. Journal of Convergence Information Technology, 6(11), 115-122.
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Conference Papers
Year Citation 2022 Kazemi Moghaddam, M., Abbasnejad, E., Wu, Q., Qinfeng Shi, J., & Van Den Hengel, A. (2022). ForeSI: Success-Aware Visual Navigation Agent. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022) (pp. 3401-3410). Waikoloa, Hawaii: IEEE.
2022 Askarian, N., Abbasnejad, E., Zukerman, I., Buntine, W., & Haffari, G. (2022). Inductive Biases for Low Data VQA: A Data Augmentation Approach. In Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2022 (pp. 231-240). IEEE.
2021 Teney, D., Abbasnejad, E., & van den Hengel, A. (2021). Unshuffling Data for Improved Generalization in Visual Question Answering. In Proceedings 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021 (pp. 1397-1407). Los Alamitos, CA: IEEE.
Scopus12021 Askarian, N., Abbasnejad, E., Zukerman, I., Buntine, W., & Haffari, G. (2021). Curriculum Learning Effectively Improves Low Data VQA. In ALTA 2021 - Proceedings of the 19th Workshop of the Australasian Language Technology Association Vol. 19 (pp. 22-33). Virtual Workshop: ACL Anthology. 2021 Zhang, M., Su, S. W., Pan, S., Chang, X., Abbasnejad, M. E., & Haffari, R. (2021). iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients. In M. Meila, & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning, PMLR Vol. 139 (pp. 12557-12566). USA: PMLR. 2021 Nassar, I., Herath, S., Abbasnejad, E., Buntine, W., & Haffari, G. (2021). All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training. In Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 7237-7246). online: IEEE.
Scopus12021 Felix Alves, R., Repasky, B., Hodge, S., Zolfaghari, R., Abbasnejad, E., & Sherrah, J. (2021). Cross-Modal Visual Question Answering for Remote Sensing Data. In DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-9). online: IEEE.
2021 Kazemi Moghaddam, M., Wu, Q., Abbasnejad, E., & Shi, J. (2021). Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021) (pp. 3732-3741). online: IEEE.
Scopus3 WoS32020 Ghassemzadeh, S., Perdomo, M. G., Abbasnejad, E., & Haghighi, M. (2020). Modelling hydraulically fractured tight gas reservoirs with an artificial intelligence (AI)-based simulator, deep net simulator (DNS). In 1st EAGE Digitalization Conference and Exhibition. online: EAGE.
2020 Abbasnejad, M., Abbasnejad, I., Wu, Q., Shi, Q., & Van Den Hengel, A. (2020). Gold seeker: Information gain from policy distributions for goal-oriented vision-and-langauge reasoning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 13447-13456). online: IEEE.
Scopus22020 Teney, D., Abbasnejad, M., & Van Den Hengel, A. (2020). Learning what makes a difference from counterfactual examples and gradient supervision. In A. Vedaldi, H. Bischof, T. Brox, & J. -M. Frahm (Eds.), Proceedings of the 16th European Conference on Computer Vision Workshops (ECCV 2020), as published in Lecture Notes in Computer Science Vol. 12355 (pp. 580-599). Switzerland: Springer.
Scopus112020 Parvaneh, A., Abbasnejad, M., Teney, D., Shi, Q., & Van Den Hengel, A. (2020). Counterfactual Vision-and-Language Navigation: Unravelling the Unseen.. In H. Larochelle, M. Ranzato, R. Hadsell, M. -F. Balcan, & H. -T. Lin (Eds.), NeurIPS Vol. 2020-December (pp. 1-12). virtual online: NIPS.
Scopus12020 Doan, B. G., Abbasnejad, M., & Ranasinghe, D. C. (2020). Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems. In Annual Computer Security Applications Conference, ACSAC 2020 (pp. 897-912). online: ACM.
Scopus16 WoS122020 Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., & Hengel, A. V. D. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Proceedings of the 34th Conference on Neural Information Processing Systems (NeruIPS 2020) Vol. abs/2005.09241 (pp. 1-11). San Francisco, CA, United States: Morgan Kaufmann.
Scopus172020 Abbasnejad, M., Teney, D., Parvaneh, A., Shi, Q., & Van Den Hengel, A. (2020). Counterfactual Vision and Language Learning.. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10041-10051). online: IEEE.
Scopus322019 Abbasnejad, M. E., Shi, Q., Van Den Hengel, A., & Liu, L. (2019). A Generative Adversarial Density Estimator.. In CVPR (pp. 10782-10791). online: Computer Vision Foundation / IEEE. 2019 Manchin, A., Abbasnejad, E., & Van Den Hengel, A. (2019). Reinforcement learning with attention that works: a self-supervised approach. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Neural Information Processing: 26th International Conference, ICONIP 2019. Proceedings, Part V Vol. 1143 CCIS (pp. 223-230). Switzerland: Springer.
Scopus13 WoS112019 Abbasnejad, M., Shi, Q., Van Den Hengel, A., & Liu, L. (2019). A generative adversarial density estimator. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 10774-10783). online: IEEE.
Scopus8 WoS22019 Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2019). What's to know? uncertainty as a guide to asking goal-oriented questions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 4150-4159). online: IEEE.
Scopus11 WoS52019 Ehsan Abbasnejad, M., Dick, A., Shi, Q., & Van Den Hengel, A. (2019). Active learning from noisy tagged images. In British Machine Vision Conference 2018, BMVC 2018. 2019 Abdi, M., Abbasnejad, E., Lim, C. P., & Nahavandi, S. (2019). 3D hand pose estimation using simulation and partial-supervision with a shared latent space. In British Machine Vision Conference 2018, BMVC 2018 (pp. 16 pages). Online: British Machine Vision Association and Society for Pattern Recognition.
Scopus42019 Abdi, M., Lim, C., Mohamed, S., Nahavandi, S., Abbasnejad, E., & Van Den Hengel, A. (2019). Discriminative clustering of high-dimensional data using generative modeling. In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) Vol. 2018-August (pp. 799-802). Windsor, Canada: IEEE.
2019 Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). Adaptive neuro-surrogate-based optimisation method for wave energy converters placement optimisation. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), as published in Lecture Notes in Computer Science (Neural Information Processing Proceedings, Part II) Vol. 11954 (pp. 353-366). Switzerland: Springer Nature.
Scopus15 WoS122018 Abbasnejad, M. E., Dick, A. R., Shi, Q., & Hengel, A. V. D. (2018). Active learning from noisy tagged images. In Proceedings of BMVC 2018 and Workshops (pp. 1-13). Newcastle upon Tyne: BMVA Press. 2018 Abedin Varamin, A., Abbasnejad, E., Shi, Q., Ranasinghe, D., & Rezatofighi, H. (2018). Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables. In MobiQuitous (pp. 1-8). online: ACM.
Scopus18 WoS112017 Rezatofighi, S., Kumar, V., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2017). DeepSetNet: Predicting sets with deep neural networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017) Vol. 2017 (pp. 5257-5266). Piscataway, NJ: IEEE.
Scopus19 WoS152017 Abbasnejad, M., Dick, A., & van den Hengel, A. (2017). Infinite variational autoencoder for semi-supervised learning. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 781-790). Honolulu: IEEE.
Scopus35 WoS202017 Niculescu-Mizil, A., & Abbasnejad, E. (2017). Label filters for large scale multilabel classification. In PMLR: Proceedings of Machine Learning Research Vol. 54 (pp. 1448-1457). Brookline, MA: Microtome Publishing.
Scopus132015 Abbasnejad, E., Domke, J., & Sanner, S. (2015). Loss-calibrated Monte Carlo action selection. In Proceedings of the National Conference on Artificial Intelligence Vol. 5 (pp. 3447-3453).
Scopus52015 Afshar, H. M., Sanner, S., & Abbasnejad, E. (2015). Linear-time Gibbs sampling in piecewise graphical models. In Proceedings of the National Conference on Artificial Intelligence Vol. 5 (pp. 3461-3467).
Scopus22013 Abbasnejad, E., Sanner, S., Bonilla, E. V., & Poupart, P. (2013). Learning community-based preferences via Dirichlet Process mixtures of Gaussian Processes. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1213-1219).
Scopus212013 Abbasnejad, M., Bonilla, E., & Sanner, S. (2013). Decision-theoretic sparsification for Gaussian process preference learning. In H. Blockeel, K. Kersting, S. Nijssen, & F. Zelezný (Eds.), Machine learning and knowledge discovery in databases, Proceedings, Part II Vol. 8189 LNAI (pp. 515-530). Prague, Czech Republic: Springer.
2013 Abbasnejad, M. (2013). Decision-theoretic approximations for machine learning. In IJCAI International Joint Conference on Artificial Intelligence (pp. 3201-3202). 2012 Sanner, S., & Abbasnejad, E. (2012). Symbolic variable elimination for discrete and continuous graphical models. In Proceedings of the National Conference on Artificial Intelligence Vol. 3 (pp. 1954-1960).
Scopus132012 Noel, J., Sanner, S., Tran, K. N., Christen, P., Xie, L., Bonilla, E. V., . . . Penna, N. D. (2012). New objective functions for social collaborative filtering. In WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web (pp. 859-868). ACM.
Scopus522009 Abbasnejad, M. E., Ramachandram, D., & Mandava, R. (2009). Optimizing kernel functions using transfer learning from unlabeled data. In 2009 2nd International Conference on Machine Vision, ICMV 2009 (pp. 111-117). Dubai, U ARAB EMIRATES: IEEE COMPUTER SOC.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2022 Co-Supervisor Causality and Deep Reinforcement Learning on Financial Trading. Doctor of Philosophy Doctorate Full Time Mr Haiyao Cao 2021 Co-Supervisor Task-Based Mapping of Human Kinematics to Robot Arm Kinematics Using Generative Adversarial Networks (GANs) Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Mr Mohamed Khalil Jabri 2021 Co-Supervisor Reinforcement Learning for Continuous Action Space Master of Philosophy Master Full Time Mr Jihua Shi 2021 Co-Supervisor Unsupervised Deep Geometry Doctor of Philosophy Doctorate Full Time Ms Xueqian Li 2021 Co-Supervisor Data efficient learning Doctor of Philosophy Doctorate Full Time Mr Yuhao Lin 2021 Co-Supervisor Towards Trustworthy Machine Learning Doctor of Philosophy Doctorate Full Time Mr Callum John Sagar Lindquist 2020 Co-Supervisor Development and validation of an integrated gait recognition system with a deep-learning architecture. Doctor of Philosophy Doctorate Full Time Mr Kayne Andrew Duncanson 2020 Co-Supervisor Symbolic and Subsymbolic Information Processing in Machine Learning Doctor of Philosophy Doctorate Full Time Boris Repasky 2020 Principal Supervisor Domain generalisation in Reinforcement Learning Master of Philosophy Master Full Time Mr Adrian John Orenstein 2019 Co-Supervisor Machine Learning and Natural Language Processing in Stock Prediction Doctor of Philosophy Doctorate Full Time Mr Jinan Zou 2019 Co-Supervisor Building Trustworthy Artificial Intelligence Systems Doctor of Philosophy Doctorate Full Time Mr Quoc Viet Vo 2018 Co-Supervisor Trustworthy Machine Learning Doctor of Philosophy Doctorate Full Time Mr Gia Bao Doan -
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2019 - 2021 Co-Supervisor A Novel Approach to Reservoir Simulation Using Supervised Learning Doctor of Philosophy Doctorate Full Time Mr Shahdad Ghassemzadeh 2018 - 2022 Co-Supervisor Interactive Vision and Language Learning Doctor of Philosophy Doctorate Full Time Mr Amin Parvaneh
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