Alan Blair's Publications

J. Gao, A. Blair & M. Pagnucco, 2023. A Symbolic-Neural Reasoning Model for Visual Question Answering, International Joint Conference on Neural Networks (to appear).

S. Iyer, A. Blair, C. White, L. Dawes, D. Moses & A. Sowmya, 2023. Vertebral compression fracture detection using imitation learning, patch based convolutional neural networks and majority voting, Informatics in Medicine Unlocked 38, 101238.

R. Purwanto, A. Pal, A. Blair & S. Jha, 2022. PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool, IEEE Transactions on Information Forensics & Security 17, 1497-1512. [10.1109/TIFS.2022.3164212]

S. Iyer, A. BLair, L. Dawes, D. Moses, C. White & A. Sowmya, 2022. Supervised and semi-supervised 3D organ localization in CT images combining reinforcement learning with imitation learning, Biomedical Physics & Engineering Express 8(3), 101238.

A. Long, W. Yin, T. Ajanthan, V. Nguyen, P. Purkait, R. Garg, A. Blair, C. Shen & A. van den Hengel, 2022. Retrieval Augmented Classification for Long Tail Visual Recognition, Computer Vision and Pattern Recognition (CVPR'22), 6959-6969. [22.CVPR])

S. Mezza, W. Wobcke & A. Blair, 2022. A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging, International Conference on Computational Linguistics (COLING'22), 542-552. [22.MWB]

X. Li & A. Blair, 2022. Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection, International Joint Conference on Neural Networks (IJCNN'22). [22.LB]

S. Iyer, A. Blair, C. White, L. Dawes, D. Moses, & A. Sowmya, 2022. Vertebral Compression Fracture detection using Multiple Instance Learning and Majority Voting, International Conference on Pattern Recognition (ICPR'22). [10.1109/ICPR56361.2022.9956309]

A. Long, A. Blair & H. van Hoof, 2022. Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation, AAAI Conference on Artificial Intelligence (AAAI'22), 7620-7627. [arXiv:2203.03078]

R. Purwanto, A. Pal, A. Blair & S. Jha, 2021. Man versus Machine: AutoML and Human Experts' Role in Phishing Detection [arXiv:2108.12193]

N. Malecki, H.-Y. Paik, A. Ignjatovic, A. Blair & E. Bertino, 2021. Simeon - Secure Federated Machine Learning Through Iterative Filtering [arXiv:2103.07704]

S. Iyer, A. Blair, L. Dawes, D. Moses, C. White & A. Sowmya, 2021. Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning [arXiv:2112.03276]

R. Purwanto, A. Pal, A. Blair & S. Jha, 2020. PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites, IEEE Conference on Communications and Network Security (CNS'20).

S. Iyer, A. Sowmya, A. Blair, C. White, L. Dawes & D. Moses, 2020. A Novel Approach to Vertebral Compression Fracture Detection Using Imitation Learning and Patch Based Convolutional Neural Network, International Symposium on Biomedical Imaging, 726 - 730.

A. Blair, 2019. Adversarial evolution and deep learning - how does an artist play with our visual system? International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMusArt'19), LNCS 11453, 18-34. [19.B]

A. Blair & A. Saffidine, 2019. AI surpasses humans at six-player poker, Science 365(6456), 864-5.

A. Hadjiivanov & A. Blair, 2019. Epigenetic evolution of deep convolutional models, IEEE Congress on Evolutionary Computation, 1478-86. [19.HB]

A. Long, J. Mason, A. Blair & W. Wang, 2019. Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal [arXiv:1905.09438]

J. Soderlund & A. Blair, 2018. Adversarial image generation using evolution and deep learning, IEEE Congress on Evolutionary Computation (CEC'18). [18.SB]
D. Vickers, J. Soderlund & A. Blair, 2017. Co-Evolving line drawings with hierarchical evolution, Australasion Conference on Artificial Life and Computational Intelligence (ACALCI'17), LNAI 10142, 39-49. [17.VSB]

A. Blair, D. Collien, D. Ripley & S. Griffith, 2017. Constructivist simulations for path search algorithms, Conference of the Australasian Association for Engineering Education (AAEE'17), 990. [17.BCRG]

A. Hadjiivanov & A. Blair, 2016. Complexity-based speciation and genotype representation for neuroevolution, IEEE Congress on Evolutionary Computation (CEC'16), 3092-3101. [16.HB]

J. Soderlund, D. Vickers & A. Blair, 2016. Parallel Hierarchical Evolution of String Library Functions, Parallel Problem Solving from Nature (PPSN'16), LNCS 9921, 281-291. [16.SVB]

D. Real & A. Blair, 2016. Learning a multi-player Chess game with TreeStrap, IEEE Congress on Evolutionary Computation (CEC'16), 617-623. [16.RB]

A. Blair, 2015. Transgenic Evolution for Classification Tasks with HERCL, Australasian Conference on Artificial Life and Computational Intelligence (ACALCI'15), LNAI 8955, 185-195. [15.B]
A. Blair, 2014. Incremental Evolution of HERCL Programs for Robust Control, Genetic and Evolutionary Computation Conference Companion (GECCO'14), 27-28. [14.B]

A. Knittel & A. Blair, 2014. Coarse and Fine Learning in Deep Networks, International Joint Conference on Neural Networks (IJCNN'14), 792-799. [14.KB]

O. Coleman, A. Blair & J. Clune, 2014. Automated Generation of Environments to Test the General Learning Capabilities of AI Agents, Genetic and Evolutionary Computation Conference (GECCO'14), 161-168. [14.OBC]

A. Knittel & A. Blair, 2014. Sparse, guided feature connections in an Abstract Deep Network [arXiv:1412.4967]

A. Blair, 2013. Learning the Caesar and Vigenere Cipher by Hierarchical Evolutionary Re-Combination, IEEE Congress on Evolutionary Computation (CEC'13), 605-612. [13.B]
A. Knittel & A. Blair, 2012. An Abstract Deep Network for Image Classification, 25th Australasian Joint Conference on Artificial Intelligence, 156-169. [12.KB] O. Coleman & A. Blair, 2012. Evolving Plastic Neural Networks for Online Learning: Review and Future Directions, 25th Australasian Joint Conference on Artificial Intelligence, 326-337. [12.CB]

D. Gurto, M. Ryan & A. Blair, 2011. Crafty: Dynamic vendor pricing in computer role-playing games, 6th International Conference on Foundations of Digital Games, 286-288. [11.GRA]
J. Veness, D. Silver, W. Uther & A. Blair, 2009. Bootstrapping from game tree search, Advances in Neural Information Processing Systems (NIPS 22), 1937-1945. [09.VSUB]

A. Blair & G. Li, 2009. Training of Recurrent Internal Symmetry Networks by Backpropagation, International Joint Conference on Neural Networks (IJCNN'09), 353--358. [09.BL]

A. Blair, 2008. Learning position evaluation for Go with internal symmetry networks, IEEE Symposium on Computational Intelligence and Games (CIG'08), 199-204. [B08] (see also [B09], unpublished).
B. Tonkes & A. Blair, 2007. Decentralised data fusion with exponentials of polynomials, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'07), 3727--3732. [07.TBa]

J. Veness & A. Blair, 2007. Effective use of transposition tables in stochastic game tree search, IEEE Symposium on Computational Intelligence and Games (CIG'07), 112-116. [07.VB]

B. Tonkes & A. Blair, 2007. Deriving Sensor Models and Non-Linear Filtering for Exponentials of Polynomials, Australasian Conference on Robotics and Automation (ACRA'07). [07.TBb]

R. Harper & A. Blair, 2006. Dynamically Defined Functions in Grammatical Evolution, IEEE Congress on Evolutionary Computation (CEC 2006), 2638-2645. [06.HBa]

K.-M. Kiang, R. Willgoss & A. Blair, 2006. Distinctness analysis on natural landmark descriptors, International Conference on Field and Service Robotics (FSR'06), 67-78. [06.KWB]

C. Phua & A. Blair, 2006. An improved minibrain that learns through both positive and negative feedback, International Joint Conference on Neural Networks (IJCNN'06), 812-819. [06.PB]

R. Harper & A. Blair, 2006. A self-selecting crossover operator, IEEE Congress on Evolutionary Computation (CEC'06), 1420--1427. [06.HBb]

K.-M. Kiang, R. Willgoss & A. Blair, 2005. Texture and distinctness analysis for natural feature extraction, Australasian Conference on Robotics and Automation (ACRA'05). [05.KWB]

R. Harper & A. Blair, 2005. A structure preserving crossover in Grammatical Evolution, IEEE Congress on Evolutionary Computation (CEC'05), 2537--2544. [05.HB]

K.-M. Kiang, R. Willgoss & A. Blair, 2004. Distinctive feature analysis of natural landmarks as a front end for SLAM applications, 2nd International Conference on Autonomous Robots and Agents (ICARA'04), 206-211. [04.KWB]
S. Chalup & A. Blair, 2003. Incremental training of first order recurrent neural networks to predict a context-sensitive language, Neural Networks 16, 955-972. [03.CB]

M. Boden & A.D. Blair, 2003. Learning the dynamics of embedded clauses, Applied Intelligence 19, 51-63. [03.BB]

J. Thomas, A. Blair & N. Barnes, 2003. Towards an efficient optimal trajectory planner for multiple mobile robots, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'03), 2291-2296. [03.TBB]

A.D. Blair & J. Ingram, 2003. Learning to predict the phonological structure of English loanwords in Japanese, Applied Intelligence 19, 101-108. [03.BI]

T. Ord & A. Blair, 2002. Exploitation and peacekeeping: introducing more sophisticated interactions to the Iterated Prisoner's Dilemma, IEEE Congress on Evolutionary Computation (CEC'02), 1606-1611. [02.OB]
J. Wiles, A.D. Blair & M. Bodén, 2001. Representation Beyond Finite States: Alternatives to Push-Down Automata, in J.F. Kolen & S.C. Kremer (Eds.) A Field Guide to Dynamical Recurrent Networks, IEEE Press, 129-142. [01.WBB]

S. Versteeg & A. Blair, 2001. Getting the job done in a hostile environment, 14th Australian Joint Conference on Artificial Intelligence, LNAI 2256, 507-518. [01.VB]

D. Shaw, N. Barnes & A. Blair, 2001. Creating Characters for Dynamic Stories in Interactive Games, International Conference on Application Development of Computer Games in the 21st Century. [01.SBB]

E. Sklar, A. Blair & J. Pollack, 2001. Training intelligent agents using human data collected on the Internet, in J.Liu, N. Zhong, Y. Tang & P. Wang (Eds.) Agent Engineering, World Scientific, 201-226. [01.SBP]

B. Tonkes, A.D. Blair & J. Wiles, 2000. Evolving learnable languages, Advances in Neural Information Processing Systems 12 (NIPS 12), MIT Press, 66-72. [00.TBW]

J. Wiles, H. Chenery, J. Hallinan, A. Blair, A. & D. Naumann, 2000. Effects of damage to the CDM Stroop model, Proc. 5th Conference of the Australasian Cognitive Science Society. [00.WCHBN]

A. Howard, A. Blair, D. Walter & E. Kazmierczak, 2000. Motion control for fast mobile robots: a trajectory-based approach, Australian Conference on Robotics and Automation (ACRA 2000). [HBWK00]

S. Chalup & A.D. Blair, 1999. Hill climbing in recurrent neural networks for learning the anbncn language, Proceedings of the Sixth International Conference on Neural Information Processing (ICONIP'99), 508-513. [99.CB]

M. Bodén, J. Wiles, B. Tonkes & A.D. Blair, 1999. Learning to predict a context-free language: Analysis of dynamics in recurrent hidden units, International Conference on Artificial Neural Networks (ICANN'99), Edinburgh, 359-364. [99.BWTB]

E. Sklar, A.D. Blair, P. Funes & J.B. Pollack, 1999. Training intelligent agents using human Internet data, Proceedings of the First Asia-Pacific Conference on Intelligent Agent Technology, World Scientific, 354-363. [99.SBFP]

A.D. Blair & E. Sklar, 1999. Exploring evolutionary learning in a simulated hockey environment, IEEE Congress on Evolutionary Computation, 197-203. [99.BS]

B. Tonkes, A.D. Blair & J. Wiles, 1999. A paradox of neural encoders and decoders or Why don't we talk backwards? Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL) LNCS 1585, 357-364. [99.TBW]

A.D. Blair, E. Sklar & P. Funes, 1999. Co-evolution, determinism and robustness, Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL) LNCS 1585, 389-396. [99.BSF]

N. Ireland & A.D. Blair, 1999. Target signal selection for a neural network based financial classifier, ICSC Symposium on Soft Computing in Financial Markets. [99.IB]

A.D. Blair, 1999. Co-evolutionary learning - lessons for human education? Fourth Conference of the Australasian Cognitive Science Society, Newcastle, Australia. [99.B]

J.B. Pollack & A.D. Blair, 1998. Co-evolution in the successful learning of Backgammon strategy, Machine Learning 32, 225-240. [98.PB]

A.D. Blair & E. Sklar, 1998. The evolution of subtle manoeuvres in simulated hockey, Fifth Conference on Simulation of Adaptive Behavior (SAB'98), Zurich, 280-285. [98.BS]

B. Tonkes, A.D. Blair & J. Wiles, 1998. Inductive bias in context-free language learning, Ninth Australian Conference on Neural Networks, Brisbane, Australia. [98.TBW]

A.D. Blair & J. Ingram, 1998. Loanword formation: a neural network approach, Proceedings of the Fourth Meeting of the ACL Special Interest Group in Computational Phonology, Montreal, 1998, 45-54. [98.BI]

E. Sklar, A.D. Blair & J.B. Pollack, 1998. Co-evolutionary learning: machines and humans schooling together, Workshop on Current Trends and Applications of Artificial Intelligence in Education, ITESM, Mexico, 98-105. [98.SBP]

J.B. Pollack & A.D. Blair, 1997. Why did TD-Gammon work? Advances in Neural Information Processing Systems (NIPS 9), 10-16. [97.PBa]

A. Blair & J. Pollack, 1997. Analysis of dynamical recognizers, Neural Computation 9(5), 1997, 1127-1142. [97.BPa]

J.B. Pollack, A.D. Blair & M. Land, 1997. Coevolution of a Backgammon player, Fifth International Conference on Artificial Life, MIT Press, 92-98. [97.PBL]

A.D. Blair & J.B. Pollack, 1997. Quasi-orthogonal maps for dynamic language recognition, Fourth International Conference on Neural Information Processing (ICONIP'97), 1065-1067. [97.BPb]

A.D. Blair & J.B. Pollack, 1997. What makes a good co-evolutionary learning environment? Australian Journal of Intelligent Information Processing Systems 4, 166-175. [97.BPc]

A.D. Blair, 1995. Two layer digital RAAM, 17th Annual Conference of the Cognitive Science Society, Pittsburgh, 478-481. [95.Ba]

A.D. Blair, 1995. Adelic path space integrals, Reviews in Mathematical Physics 7(1), 1995, 21-49. [95.Bb]

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