Selected Publications [Full list in Google Scholar]

• M. Zhang, S. Su, S. Pan, X. Chang, E. Abbasnejad, G. Haffari, iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients,  International Conference on Machine Learning (ICML), 2021.
  
• I. Nassar, S. Herath, E. Abbasnejad, W. Buntine, G. Haffari, All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training,  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), [Oral], 2021. [Link]
  
• A. Parvaneh, E. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead,  IEEE Transactions on Multimedia, 2021. [Link]
  
• M. Neshata, M. Nezhad, E. Abbasnejad, S. Mirjalilide, D. Groppi, A. Heydari, L. Tjernberg,D. Garcia, B. Alexandera, Q. Shi, M. Wagner, Wind turbine power output prediction using a new hybrid neuro-evolutionary method,  Energy, Volume 229, 2021. [Link]
  
• M. Neshata, M. Nezhad, E. Abbasnejad, S. Mirjalilide, L. Tjernberg,D. Garcia, B. Alexandera, M. Wagner, 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, 2021. [Link]
  
• A. Parvaneh, E. Abbasnejad, D. Teney, Q. Shi, A. van den Hengel, Counterfactual Vision-and-Language Navigation: Unraveling the Unseen,  Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. [Spotlight] [Link]
  
• D. Teney, K. Kafle, R. Shrestha, E. Abbasnejad, C. Kanan, A. van den Hengel, On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law, Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. [Link]
• D. Teney, E. Abbasnejad, A. van den Hengel, Learning what makes a difference from counterfactual examples and gradient supervision, European Conference on Computer Vision (ECCV), 2020 [Link]
• E. Abbasnejad, D. Teney, A. Parvaneh, Q. Shi, A. van den Hengel, Counterfactual Vision and Language Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Oral][Link]
• E. Abbasnejad, I. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Language Reasoning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Link]
• B. Doan, E. Abbasnejad, D. Ranasinghe, Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network, 36th Annual Computer Security Applications Conference (ACSAC), 2020. [Link]
• M. Kazemi, Q. Wu, E. Abbasnejad, Q. Shi, Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021. [Link]
• E. Abbasnejad, Q. Shi, A. van den Hengel, L. Liu, GADE: A Generative Adversarial Approach to Density Estimation and its Applications, International Journal of Computer Vision 128 (10), 2731-2743, 2020. [Link]
• E. Abbasnejad, Q. Shi, A. van den Hengel, L. Liu, A Generative Adversarial Density Estimator, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019. [Oral] [Link]
• E. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, L. Liu, What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019. [Link]
• E. Abbasnejad, A. Dick, Q. Shi, A. van den Hengel, Active Learning from Noisy Tagged Images, In Proceedings of British Machine Vision Conference (BMVC), Newcastle, UK, 2018. [Link]
• M. Abdi, E. Abbasnejad, C. P. Lim, S. Nahavandi, 3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space, In Proceedings of British Machine Vision Conference (BMVC). Newcastle, UK, 2018. [Oral] [Link]
• A. Abedin, E. Abbasnejad, Q. Shi, D. Ranasinghe, H. Rezatofighi, Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables , In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems (MobiQuitous), New York, NY, USA, 2018. [Link]
  
• E. Abbasnejad, A. Dick, A. van den Hengel, Infinite Variational Autoencoder for Semi-Supervised Learning , The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017. [link] [supplement] 
  
• A. Niculescu-Mizil, E. Abbasnejad, Label Filters for Large Scale Multilabel Classification , In Proceedings of The 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, USA, 2017. [Link] 
• H. Rezatofighi, V. Kumar, A. Milan, E. Abbasnejad, A. Dick, I. Reid, DeepSetNet: Predicting Sets with Deep Neural Networks, The IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017. [link]
• A. Khoshkbarforoushha, R. Ranjan, R. Gaire, E. Abbasnejad, L. Wang, A. Zomaya, Distribution Based Workload Modelling of Continuous Queries in Clouds , In IEEE Transactions on Emerging Topics in Computing, 2016. [link]
  
• E. Abbasnejad, J. Domke, S. Sanner, Loss-calibrated Monte Carlo Action Selection , In Proceedings of the 26th Conference on Artificial Intelligence (AAAI), Austin, USA, 2015. [link]
• H. Afshar, S. Sanner, E. Abbasnejad, Linear-time Gibbs Sampling in Piecewise Graphical Models , In Proceedings of the 26th Conference on Artificial Intelligence (AAAI), Austin, USA, 2015. [link]
  
• E. Abbasnejad, E. V. Bonilla, S. Sanner, Decision-theoretic Sparsification for Gaussian Process Preference Learning , Proceedings of the Machine Learning and Knowledge Discovery in Databases - European Conference (ECML PKDD). Prague, Czech Republic, 2013. [link]
• E. Abbasnejad, S. Sanner, E. V. Bonilla, P. Poupart, Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes , In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013. [link] [dataset]