Monash Faculty of IT projects receive recognition in globally renowned AI conference
The Department of Data Science and Artificial Intelligence, in the Faculty of Information Technology (IT) at Monash University, has had 13 of it’s academic project papers accepted into the thirty-fifth Conference on Artificial Intelligence. Marking a 160% increase in accepted papers from the previous year.
The Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence, will be held in Vancouver, Canada, from 2–9 February, and promotes theoretical and applied AI research as well as intellectual interchange among researchers and practitioners.
The conference program will feature substantial, original research and practices. As well as panel discussions and invited presentations which identify significant social, philosophical, and economic issues influencing AI’s development throughout the world.
The projects from the Department of Data Science and Artificial Intelligence which will be presented at this week's conference include seven from the Optimisation Discipline Group, four from the Vision and Language Discipline Group, one from the Computational and Collective Intelligence Discipline Group and one from the Machine Learning Group.
“On behalf of the Department, I’d like to congratulate these academics on this well-deserved achievement. Being accepted into this conference not only acknowledged their hard work but also their dedication to the field of AI,” said Professor Jianfei Cai, Head of the Department of Data Science and Artificial Intelligence.
Professor in the Department of Data Science and Artificial Intelligence and a Fellow of the AAAI, Peter Stuckey also acknowledged the team on their achievement.
“To have 13 research papers accepted into a top AI conference like the AAAI is a fantastic achievement and showcases the department’s strong AI expertise. Well done to everyone on this effort,” added Professor Stuckey.
The entire list of accepted papers includes:
- Curriculum-Meta Learning for Order-Robust Continual Relation Extraction, Tongtong Wu, Xuekai Li, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Yujin Zhu, Guoqiang Xu
- Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning, Sheng Wan, Shirui Pan, Jian Yang, Chen Gong
- SA-BNN: State-Aware Binary Neural Network, Chunlei Liu, Peng Chen, Bohan Zhuang, Chunhua Shen, Baochang Zhang, Wenrui Ding
- Towards Balanced Defect Prediction with Better Information Propagation, Xianda Zheng, Yuan-Fang Li, Huan Gao, Yuncheng Hua, Guilin Qi
- A Fast Exact Algorithm for the Resource Constrained Shortest Path Problem, Saman Ahmadi, Guido Tack, Daniel Harabor, Philip Kilby
- f-Aware Conflict Prioritization & Improved Heuristics for Conflict-Based Search, Eli Boyarski, Ariel Felner, Pierre Le Bodic, Daniel Harabor, Peter Stuckey, Sven Koenig
- Optimal Decision Trees for Nonlinear Metrics, Emir Demirović, Peter Stuckey
- Cutting to the Core of Pseudo-Boolean Optimization: Combining Core-Guided Search with Cutting Planes Reasoning, Jo Devriendt, Stephan Gocht, Emir Demirović, Peter Stuckey, Jakob Nordström
- Symmetry Breaking for k-Robust Multi-Agent Path Finding, Zhe Chen, Daniel Harabor, Jiaoyang Li, Peter Stuckey
- A Scalable Two Stage Approach to Computing Optimal Decision Sets, Alexey Ignatiev, Edward Lam, Peter Stuckey, Joao Marques-Silva
- Optimising Automatic Calibration of Electric Muscle Stimulation, Graeme Gange, Jarrod Knibbe
- A Unified Framework for Planning with Learned Neural Network Transition Models, Buser Say
- Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness Anh Tuan Bui, Trung Le, He Zhao, Paul Montague, Olivier DeVel, Tamas Abraham, Dinh Phung
To learn more about the conference, please visit here.