Dr Ehsan Abbasnejad
Senior Lecturer
Australian Institute for Machine Learning
Division of Research and Innovation
Dr. Ehsan Abbasnejad is a senior lecturer and Future Making Fellow at the University of Adelaide. His research interests include computer vision, natural language processing, and machine learning. He has published several papers in these fields and has proposed new methods for density estimation, active learning, and overcoming the simplicity bias. You can find more information about his research on his website: https://ehsanabb.github.io/
Some of his notable papers are:
-
“GADE: A Generative Adversarial Approach to Density Estimation and its Applications” : This paper proposes a new generative adversarial approach to density estimation that can be used for various applications such as image synthesis, anomaly detection, and data augmentation. The proposed method is evaluated on several datasets and compared with other state-of-the-art methods.
-
“Active Learning by Feature Mixing” : This paper proposes a new active learning method that can select the most valuable samples to be labeled in the training process iteratively. The proposed method is evaluated on several datasets and compared with other state-of-the-art methods.
-
“Evading the Simplicity Bias: Training a Diverse Set of Models Discovers …” : This paper proposes a new method to overcome the simplicity bias by learning a collection of diverse predictors. The proposed method is evaluated on several datasets and compared with other state-of-the-art methods.
He is an area chair for the Conference on Computer Vision and Pattern (CVPR) and Neural Information Processing Systems (NeurIPS).
-
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
-
Journals
Year Citation 2023 Doan, B. G., Yang, S., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Ranasinghe, D. C. (2023). Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37, 14783-14791.
Scopus22023 Duncanson, K. A., Thwaites, S., Booth, D., Hanly, G., Robertson, W. S. P., Abbasnejad, E., & Thewlis, D. (2023). Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data. Sensors, 23(7), 3392.
Scopus2 WoS1 Europe PMC12023 Teney, D., Lin, Y., Oh, S. J., & Abbasnejad, E. (2023). ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets. Advances in Neural Information Processing Systems, 36, 20 pages.
Scopus52022 Doan, B. G., Xue, M., Ma, S., Abbasnejad, E., & Ranasinghe, D. C. (2022). TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems. IEEE Transactions on Information Forensics and Security, 17, 3816-3830.
Scopus21 WoS92022 Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2022). Show, price and negotiate: a negotiator with online value look-ahead. IEEE Transactions on Multimedia, 24, 1426-1434.
Scopus22021 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.
Scopus13 WoS72021 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.
Scopus152 WoS832021 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.
Scopus80 WoS512020 Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., & van den Hengel, A. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 33, 11 pages. 2020 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.
Scopus2 WoS22019 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 Abbasnejad, M., Shi, Q., Abbasnejad, I., Hengel, A., & Dick, A. (2017). Bayesian Conditional Generative Adverserial Networks.. CoRR, abs/1706.05477. 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.
Scopus432013 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).
Scopus152013 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 WoS12012 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.
Scopus39 WoS302011 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.
Scopus62011 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.
Scopus2 -
Conference Papers
Year Citation 2024 Herath, S., Fernando, B., Abbasnejad, E., Hayat, M., Khadivi, S., Harandi, M., . . . Haffari, G. (2024). Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation. In Proceedings of the IEEE International Conference on Computer Vision (pp. 11619-11628). Online: IEEE.
DOI Scopus32024 Doan, B. G., Nguyen, D. Q., Montague, P., Abraham, T., De Vel, O., Camtepe, S., . . . Ranasinghe, D. C. (2024). Bayesian Learned Models Can Detect Adversarial Malware for Free. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14982 LNCS (pp. 45-65). Bydgoszcz: Springer Nature Switzerland.
DOI2024 Vo, V. Q., Abbasnejad, E., & Ranasinghe, D. C. (2024). Brusleattack: a query-efficient scorebased black-box sparse adversarial attack. In Proceedings of the 12th International Conference on Learning Representations (ICLR 2024) (pp. 1-38). Online: ICLR. 2024 Zou, J., Guo, M., Tian, Y., Lin, Y., Cao, H., Liu, L., . . . Shi, J. Q. (2024). Semantic Role Labeling Guided Out-of-distribution Detection. In 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 14641-14651). Online: European Language Resources Association (ELRA). 2024 Teney, D., Wang, J., & Abbasnejad, E. (2024). Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup. In Proceedings of Machine Learning Research Vol. 235 (pp. 47948-47964). Vienna: OpenReview.net. 2023 Doan, B. G., Yang, S., Montague, P., De Vel, O., Abraham, T., Camtepe, S., . . . Ranasinghe, D. C. (2023). Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness. In B. Williams, Y. Chen, & J. Neville (Eds.), THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 12 (pp. 14783-14791). Online: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2023 Jabri, M. K., Papadakis, P., Abbasnejad, E., Coppin, G., & Shi, J. (2023). Improving Reward Estimation in Goal-Conditioned Imitation Learning with Counterfactual Data and Structural Causal Models. In Proceedings of the International Conference on Informatics in Control, Automation and Robotics Vol. 2 (pp. 329-337). Online: SCITEPRESS - Science and Technology Publications.
DOI2023 Xu, H. M., Liu, L., Chen, H., Abbasnejad, E., & Felix, R. (2023). Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models. In Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 (pp. 3284-3294). Online: IEEE.
DOI2023 McDonnell, M. D., Gong, D., Parvaneh, A., Abbasnejad, E., & Hengel, A. V. D. (2023). RanPAC: Random Projections and Pre-trained Models for Continual Learning.. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), NeurIPS Vol. 36 (pp. 32 pages). Online: Neural information processing systems foundation.
Scopus92023 Nassar, I., Hayat, M., Abbasnejad, E., Rezatofighi, H., Harandi, M., & Haffari, G. (2023). LAVA:Label-efficient Visual Learning and Adaptation. In Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023 (pp. 147-156). Online: IEEE.
DOI Scopus12023 Abbasnejad, I., Zambetta, F., Salim, F., Wiley, T., Chan, J., Gallagher, R., & Abbasnejad, E. (2023). SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2023-June (pp. 1111-1120). Vancouver, BC, Canada: IEEE.
DOI Scopus32023 Nassar, I., Hayat, M., Abbasnejad, E., Rezatofighi, H., & Haffari, G. (2023). Protocon: Pseudo-Label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-Supervised Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 11641-11650). Online: IEEE.
DOI Scopus132022 Teney, D., Peyrard, M., & Abbasnejad, E. (2022). Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning.. In CoRR Vol. abs/2207.02598. 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). Online: IEEE.
DOI Scopus42022 Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Hengel, A. V. D., & Shi, Q. J. (2022). Active Learning by Feature Mixing.. In CoRR Vol. abs/2203.07034. 2022 Zou, J., Cao, H., Liu, Y., Liu, L., Abbasnejad, E., & Shi, J. Q. (2022). UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining. In FinNLP 2022 - 4th Workshop on Financial Technology and Natural Language Processing, Proceedings of the Workshop (pp. 122-126). Online: Association for Computational Linguistics (ACL).
Scopus12022 Zou, J., Cao, H., Liu, L., Lin, Y., Abbasnejad, E., & Shi, J. Q. (2022). Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model. In FinNLP 2022 - 4th Workshop on Financial Technology and Natural Language Processing, Proceedings of the Workshop (pp. 178-186). Online: Association for Computational Linguistics (ACL).
Scopus32022 Zhang, Z., Ng, I., Gong, D., Liu, Y., Abbasnejad, E. M., Gong, M., . . . Shi, J. Q. (2022). Truncated Matrix Power Iteration for Differentiable DAG Learning. In Advances in Neural Information Processing Systems Vol. 35 (pp. 13 pages). Online: Neural information processing systems foundation.
Scopus102022 Xu, H. M., Liu, L., & Abbasnejad, E. (2022). Progressive Class Semantic Matching for Semi-supervised Text Classification. In M. Carpuat, M. -C. De Marneffe, & I. V. Meza Ruiz (Eds.), NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 3003-3013). Seattle, Washington & Online: Association for Computational Linguistics.
DOI Scopus92022 Teney, D., Peyrard, M., & Abbasnejad, E. (2022). Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning. In Conferenc proceedings Computer Vision ECCV Vol. 13683 (pp. 458-476). Tel Aviv, Israel: Springer Nature Switzerland.
DOI Scopus6 WoS22022 Doan, B. G., Abbasnejad, E., Shi, J. Q., & Ranashinghe, D. C. (2022). Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. In INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162 Vol. 162 (pp. 15 pages). Baltimore, MD: JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022 Teney, D., Abbasnejad, E., Lucey, S., & Hengel, A. V. D. (2022). Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) Vol. 2022-June (pp. 16740-16751). New Orleans, Louisiana: IEEE.
DOI Scopus34 WoS52022 Vo, V. Q., Abbasnejad, M., & Ranasinghe, D. C. (2022). RamBoAttack: A Robust and Query Efficient Deep Neural Network Decision Exploit. In Proceedings 2022 Network and Distributed System Security Symposium Vol. 2022 (pp. 1-18). Online: Internet Society.
DOI Scopus62022 Vo, V. Q., Abbasnejad, E., & Ranasinghe, D. C. (2022). QUERY EFFICIENT DECISION BASED SPARSE ATTACKS AGAINST BLACK-BOX DEEP LEARNING MODELS. In ICLR 2022 - 10th International Conference on Learning Representations. Online: International Conference on Learning Representations, ICLR.
Scopus132022 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). Online: IEEE.
DOI Scopus7 WoS12022 Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Van Den Hengel, A., & Shi, J. Q. (2022). Active Learning by Feature Mixing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 12227-12236). New Orleans, LA, USA: IEEE.
DOI Scopus61 WoS132021 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.
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.
DOI Scopus32021 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.
DOI Scopus422021 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.
Scopus262021 Teney, D., Abbasnejad, E., Lucey, S., & Hengel, A. V. D. (2021). Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization.. In CoRR Vol. abs/2105.05612. 2021 Teney, D., Abbasnejad, E., & van den Hengel, A. (2021). Unshuffling Data for Improved Generalization in Visual Question Answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021) Vol. abs/2002.11894 (pp. 1397-1407). Los Alamitos, CA: IEEE.
DOI Scopus37 WoS52021 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.
DOI Scopus13 WoS82020 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 (pp. 1-5). online: EAGE.
DOI Scopus22020 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.
DOI Scopus32020 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.
DOI Scopus502020 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.
Scopus242020 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.
DOI Scopus158 WoS582020 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.
Scopus712020 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.
DOI Scopus932019 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.
DOI Scopus35 WoS212019 Abbasnejad, M. E., 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.
DOI Scopus14 WoS62019 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.
DOI Scopus17 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., 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.
DOI2019 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.
DOI Scopus24 WoS162019 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.
DOI Scopus62018 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.
DOI Scopus37 WoS222017 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.
DOI Scopus28 WoS182017 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.
DOI Scopus59 WoS312017 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.
Scopus222015 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).
Scopus32013 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).
Scopus262013 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.
DOI2013 Abbasnejad, M. E. (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).
Scopus172012 Sanner, S., & Abbasnejad, E. (2012). Symbolic Variable Elimination for Discrete and Continuous Graphical Models. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 1954-1960).
Scopus212012 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.
DOI Scopus542009 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.
DOI Scopus3 WoS2 -
Preprint
Year Citation 2019 Parvaneh, A., Abbasnejad, E., Wu, Q., & Shi, J. (2019). Show, Price and Negotiate: A Hierarchical Attention Recurrent Visual Negotiator..
- Learning to Reason in Reinforcement Learning, Discovery Project, Australian Research Council, 2024-2027--$544K
- Learning to Model Human Trust, AmpX–$200K, 2023-2027
- Learning in Open-ended Tasks, Naval Group–$175K, 2023-2027
- Unlocking wheat grain heterogeneity using machine vision, Biotechnology and Biological Sciences (BBSRC), UK–£2.4M 2022-2026
- Detecting objects left behind, CERTIS Group–$100K, 2022-2023
- Future Making Fellowship, University of Adelaide–$500K, 2022-2025
- Bushfire resilience, fuelled by artificial intelligence, Citizen Science Grant $498K, 2021-2023
- Talos: Towards Trustworthy Machine Learning Models, DSTG Fund–$1M, 2021-2023
- Learning to Learn and Adapt with Less Labels, DARPA–$US2.2M, 2019-2023
- Intelligent Decision Superiority through Vision and Language Technology, DSTG Fund–$US500K, 2020-2022
- Extracting Value from Crop/soil Variability Mapping, Grains Research and Development Corporation–$1M, 2020-2021
- Machine Learning for Irrigation and Soil Management, Grains Research and Development Corporation–$1M, 2020-2021
- Learning from Demonstrations using GANs, IMT Atlantique & UofA–€48K & $40K, 2020-2023
- Citrus Irrigation Scheduling using Hyperspectral Imagery and Machine Learning–$265K, 2020
- AI for Farm Management, Wine Australia and Riverland Wine– $2.195M ($588,190 for ML), 2020-2022
- Modelling Team Sport Strategies using Generative Adversarial Networks, Australian Institute of Sport (AIS)–$120K, 2019-2020
- Player Performance Analysis, South Australian Sports Institute–$55K, 2019-2020
- Object Detection with Less Data, Australian Geospatial-Intelligence Organisation–$100K, 2019-2020
- Introduction to Statistical Machine Learning, The University of Adelaide, 2022, 2023
- Foundations of Computer Science, The University of Adelaide, 2021
- Deep Learning Fundamentals, The University of Adelaide, 2021
- Applications of AI and Machine Learning, The University of Adelaide, 2021
- Distributed Systems and Data Mining, The University of Adelaide, 2020
- Mining Big Data, The University of Adelaide, 2020
- Introduction to Computer Vision, The University of Adelaide, 2019
- Scientific Computing, The University of Adelaide, 2018
-
Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2024 Principal Supervisor Improving Machine Learning Models on the Out-Of-Distribution Generalization Doctor of Philosophy Doctorate Full Time Miss Seyedeh Mahdieh Mirmahdi 2024 Co-Supervisor Robust Machine Learning Techniques for RF Signal Classification on Sparse and Noisy Digitally Sampled Radar Data Master of Philosophy Master Full Time Mr Sebastian Luke McCormack Cocks 2024 Co-Supervisor Fine-Grained Explainable Classification and Recognition for Ship Identification Doctor of Philosophy Doctorate Full Time Mr Ignacio Alejandro Meza De La Jara 2023 Co-Supervisor Learning to Reason and Generalise Using Multimodal Approaches Doctor of Philosophy Doctorate Full Time Mr Luke Thomas Heffernan 2023 Principal Supervisor Robot Action Planning with Affordances and Large Language model Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Mr Gaelic Jean Edouard Bechu 2023 Co-Supervisor Distributed learning on connected devices with limited resources Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Mr Lucas Fentanes Machado 2022 Co-Supervisor Causality and Deep Reinforcement Learning on Financial Trading. Doctor of Philosophy Doctorate Full Time Mr Haiyao Cao 2020 Co-Supervisor Development and validation of an integrated gait recognition system with a deep-learning architecture. Doctor of Philosophy Doctorate Part Time Mr Kayne Andrew Duncanson 2020 Co-Supervisor Symbolic and Subsymbolic Information Processing in Machine Learning Doctor of Philosophy Doctorate Full Time Boris Repasky -
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2019 - 2023 Co-Supervisor Machine Learning and Natural Language Processing in Stock Prediction Doctor of Philosophy Doctorate Full Time Mr Jinan Zou 2019 - 2023 Co-Supervisor Towards Robust Deep Neural Networks: Query Efficient Black-Box Adversarial
Attacks and DefencesDoctor of Philosophy Doctorate Full Time Mr Quoc Viet Vo 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 2018 - 2022 Co-Supervisor Towards Robust Deep Neural Networks Doctor of Philosophy Doctorate Full Time Mr Gia Bao Doan
Connect With Me
External Profiles
