[Source: rdn-consulting]  

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 AI Future

Our goal is to develop generalist AI systems that can learn, reason, and act meaningfully across all practical scenarios in an advanced civilization, operating across scales in space-time.

Scaling out: Agents as digital spieces

Instead of scaling up a single agent, we envision a future 'society of agents' representing a new kind of digital species. Each agent develops through lifewide experiences, interacting and collaborating with other agents and humans while evolving continuously with its dynamic environments. These agents are equipped with advanced cognitive capabilities: multimodal perception, episodic and semantic memory, statistical relational learning, theory of mind, common sense reasoning, and knowledge integration. Through their interactions, they develop collective intelligence and cultural evolution, adapting their behaviors and knowledge based on societal needs. This distributed approach enables more robust, adaptable, and aligned AI systems that can address complex challenges across different domains.

Reasoning: Augmented System 2 capability

We are concerned with learning the capability to deduce new knowledge from previously acquired knowledge in response to a query. Such behaviors can be demonstrated naturally using a symbolic system with a rich set of inferential tools, given that the symbols can be grounded in the sensory world. Deep learning contributes to the bottom-up learning of such a reasoning system by resolving the symbol grounding problem. Our research aims to build neural architectures that can learn to exhibit high-level reasoning functionalities, for example, answering new questions across space-time in a compositional and progressive fashion.

Alignment: Truth, values, safety and civilization

• Learning and reasoning on multisensor data ($850K), Australian DoD, 2022-2024.
• Framework for verifying machine learning algorithms ($360K), ARC Discovery, 2021-2023.
• Defence applied AI experiential CoLab ($1M), Australian DoD, 2020-2021.
• Telstra centre of excellence in big data and machine learning ($1.6M), Telstra, 2016–2020.
• Predicting hazardous software using deep learning, ($100K), Samsung GRO, 2016–2017.
• Studying and developing advanced machine learning based models for extracting chemical/drug-disease relations from biomedical literature”, ($54K), Vietnam NAFOSTED, 2017–2018.
• Building a simulator of mail sorting machine, ($12K), PTIT VN, 2003.

• Deep learning and reasoning: Recent advances, Tutorial @VIASM Summer School in DL, July 2023.
• Deep analytics via learning to reason Keynote @ACOMPA, Nov 2022.
• Neural machine reasoning A tutorial @IJCAI, August 2021.
• Deep learning 2.0 Keynote @FPT AI Conference, August 2021.
• From deep learning to deep reasoning A tutorial @KDD, August 2021.
• Deep learning 1.0 and beyond, A tutorial @IEEE SSCI, Canberra, Dec 2020.
• Machine reasoning, @Monash University, August 2020.
• Machine reasoning at A2I2, @A2I2 reading group, April 2020.
• Machines that learn to talk about what they see, Keynote at NICS'19, Hanoi, Vietnam, Dec 2019.
• Memory advances in Neural Turing Machines, AI Day, Hanoi, Vietnam, June 2019.
• Empirical AI Research , @International University, HCM City, Vietnam, May 2019.
• Representation learning on graphs, Keynote at MAPR, HCM City, Vietnam, May 2019.
• Advances in Neural Turing Machines, @CafeDSL, University of Wollongong, Aug 2018.
• Deep learning for detecting anomalies and software vulnerabilities, @ACT, Hanoi, Jan 2017.
• Deep architecture engineering, @HUST & VNU-UET, Hanoi, Jan 2017.
• Deep learning, @WEHI, Melbourne, Dec 2016.
• Deep learning tutorials at AusDM'16 in Canberra, AI'16 in Hobart.
• Deep learning for non-cognitive domains, @DSL, University of Wollongong, Aug 2016.

• The dynamics of knowledge, Truyen Tran, Medium, October 2024.
  
• AI development and education, Truyen Tran, Medium, Sep 2024.
• The shifting landscape of modern AI, Truyen Tran, Medium, Aug 2024. 
• AI, math, medicine, software, and the sciences, Truyen Tran, Medium, August 2024. 
• AI: Be careful what you wish for, or even the rich cry, Truyen Tran, Medium, April 2023.
• Very big text models, Truyen Tran, Medium, Feb 2023.
• On expressiveness, learnability and generalizability of deep learning, Truyen Tran, Blogger, Jan 2017.
• Making a dent in AI, or how to play a fast ball game, Truyen Tran, Blogger, Dec 2016.
• RBMs: A brief 30 year history of a Swiss army knife, Truyen Tran, Blogger, Dec 2016.
• Machine learning in three lines, Truyen Tran, Blogger, Dec 2016.
• Machine learning: Four years after the turning point, Truyen Tran, Blogger, Dec 2016.
• Machine learning at its turning point: Non-convexity, Truyen Tran, Blogger, May 2012.
• Data size matters, Truyen Tran, Blogger, Aug 2008.

• On size fit many: Column bundle for multi-X learning, Trang Pham, Truyen Tran, Svetha Venkatesh. arXiv preprint arXiv: 1702.07021.
• Learning deep matrix representations, Kien Do, Truyen Tran, Svetha Venkatesh, arXiv preprint arXiv:1703.01454.
• Relational dynamic memory networks, Trang Pham, Truyen Tran, Svetha Venkatesh, arXiv preprint arXiv:1808.04247.
• Logically consistent loss for visual question answering, Anh-Cat Le-Ngo, Truyen Tran, Santu Sana, Sunil Gupta, Svetha Venkatesh, arXiv preprint arXiv:2011.10094.

Publications

• MP-PINN: A Multi-Phase Physics-Informed Neural Network for epidemic forecasting, Thang Nguyen, Dung Nguyen, Kha Pham, Truyen Tran, AusDM'24.
• Promptable iterative visual refinement for video instance segmentation, Tuyen Tran, Thao Minh Le, Truyen Tran, ECCV WS on IRL, 2024.
• Unified compositional query machine with multimodal consistency for video-based human activity recognition, Tuyen Tran, Thao Minh Le, Duy Hung Tran, Truyen Tran, BMVC'24.
• Revisiting the dataset bias problem from a statistical perspective, Do, Kien, Dung Nguyen, Hung Le, Thao Le, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, and Svetha Venkatesh, ECAI'24.
• Diversifying training pool predictability for zero-shot coordination: A theory of mind approach, Dung Nguyen, Hung Le, Kien Do, Svetha Venkatesh, Truyen Tran, IJCAI, 2024.
• Root cause explanation of outliers under noisy mechanisms, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Thin Nguyen, SvethaVenkatesh, AAAI'24. 
• Hierarchical GFlowNet for crystal structure generation, Nguyen, Tri, Sherif Tawfik, Truyen Tran, Sunil Gupta, Santu Rana, and Svetha Venkatesh. In AI for Accelerated Materials Design-NeurIPS 2023 Workshop. 2023.
• Dynamic reasoning for Movie QA: A character-centric approach, Long Hoang Dang, Thao Minh Le, Vuong Le, Tu Minh Phuong, Truyen Tran, IEEE Transactions on Multimedia, 2023.
• Balanced Q-learning: Combining the influence of optimistic and pessimistic targets, Thommen George Karimpanal, Hung Le, Majid Abdolshah, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh, Artificial Intelligence, 2023.
  
• Persistent-transient duality: A multi-mechanism approach for modeling human-object interaction, Hung Tran, Vuong Le, Svetha Venkatesh, Truyen Tran, ICCV'23.
• Compositional prompting with successive decomposition for multimodal language models, Long Hoang Dang, Thao Minh Le, Tu Minh Phuong and Truyen Tran, KDD'23 workshop on LLM4AI: Theories and Applications in Large-scale AI Models, 2023.
• Social motivation for modelling other agents under partial observability in decentralised training, Dung Nguyen, Hung Le, Kien Do, Svetha Venkatesh, Truyen Tran, IJCAI, 2023.
  
• Improving out-of-distribution generalization with indirection representations, Pham, Kha, Hung Le, Man Ngo, and Truyen Tran, ICLR'23.
• Memory-augmented theory of mind network, Nguyen, Dung, Phuoc Nguyen, Hung Le, Kien Do, Svetha Venkatesh, and Truyen Tran, AAAI'23.
• Guiding visual question answering with attention priors, Le, Thao Minh, Vuong Le, Sunil Gupta, Svetha Venkatesh, and Truyen Tran. WACV'23.
• Momentum adversarial distillation: Handling large distribution shifts in data-free knowledge distillation, Do, Kien, Thai Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, and Svetha Venkatesh, NeurIPS'22.
• Functional indirection neural estimator for better out-of-distribution generalization, Pham, Kha, Thai Hung Le, Man Ngo, and Truyen Tran, NeurIPS'22.
• Time-evolving conditional character-centric graphs for movie understanding, Long Hoang Dang, Thao Minh Le, Vuong Le, Tu-Minh Phuong, and Truyen Tran, NeurIPS 2022 Temporal Graph Learning Workshop.
  
• Video dialog as conversation about objects living in space-time, Pham, Hoang-Anh, Thao Minh Le, Vuong Le, Tu Minh Phuong, and Truyen Tran. ECCV'22.
• Towards effective and robust neural Trojan defenses via input filtering, Do, Kien, Haripriya Harikumar, Hung Le, Dung Nguyen, Truyen Tran, Santu Rana, Dang Nguyen, Willy Susilo, and Svetha Venkatesh. ECCV'22.
• Learning to transfer role assignment across team sizes,  Dung Nguyen, Phuoc Nguyen, Svetha Venkatesh, Truyen Tran, AAMAS, 2022.
• Learning theory of mind via dynamic traits attribution, Dung Nguyen, Phuoc Nguyen, Hung Le, Kien Do, Svetha Venkatesh, Truyen Tran, AAMAS, 2022. 
• Model-based episodic memory induces dynamic hybrid controls, Hung Le, Thommen K George, Majid Abdolshah, Truyen Tran, Svetha Venkatesh, NeurIPS'21.
• DeepProcess: Supporting business process execution using a MANN-based recommender system, Asjad Khan, Aditya Ghose, Hoa Dam, Hung Le, Truyen Tran, Kien Do, ICSOC'21.
• From deep learning to deep reasoning, Truyen Tran, Vuong Le, Hung Le, Thao Le, KDD, 2021.
• Knowledge distillation with distribution mismatch, D Nguyen, S Gupta, T Nguyen, S Sana, P Nguyen, T Tran, KL Le, S Ryan, ... ECML-PKDD'21, 2021
• Fast conditional network compression using Bayesian HyperNetworks, P Nguyen, T Tran, KL Le, S Gupta, S Sana, D Nguyen, T Nguyen, S Ryan, ... ECML-PKDD'21, 2021
• Variational hyper-encoding networks, P Nguyen, T Tran, S Gupta, S Rana, HC Dam, S Venkatesh, ECML-PKDD'21, 2021
• Hierarchical conditional relation networks for multimodal video question answering, TM Le, V Le, S Venkatesh, T Tran, International Journal of Computer Vision, 2021
• Hierarchical object-oriented spatio-temporal reasoning for video question answering, LH Dang, TM Le, V Le, T Tran, IJCAI'21
• Object-centric representation learning for video question answering Long Hoang Dang, Thao Minh Le, Vuong Le, Truyen Tran, IJCNN'21. 
• Learning asynchronous and sparse human-object interaction in videos Romero Morais, Vuong Le, Svetha Vekatesh, Truyen Tran, CVPR'21. 
• Goal-driven long-term trajectory prediction, Hung Tran, Vuong Le, Truyen Tran, WACV'21. 
• Automatically recommending components for issue reports using deep learning, Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Chaiyong Ragkhitwetsagul & Aditya Ghose , Empirical Software Engineering volume 26, Article number: 14 (2021).
• Semi-supervised learning with variational Bayesian inference and maximum uncertainty regularization, Kien Do, Truyen Tran and Svetha Venkatesh,  AAAI'21.
  
• Toward a generalization metric for deep generative models, Thanh-Tung, Hoang, and Truyen Tran. NeurNIPS 2020 1st Workshop on I Can’t Believe It’s Not Better.
• GEFA: Early Fusion Approach in Drug-Target Affinity Prediction, Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, Truyen Tran, Machine Learning for Structural Biology (MLSB) Workshop at NeurIPS 2020.
• HyperVAE: A minimum description length variational hyper-encoding network, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Hieu-Chi Dam, Svetha Venkatesh, NeurIPS 2020 Workshop on Meta-Learning
• Unsupervised anomaly detection on temporal multiway data, Duc Nguyen, Phuoc Nguyen, Kien Do, Santu Rana, Sunil Gupta, Truyen Tran, 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (SSCI 2020).
• Theory of mind with guilt aversion facilitates cooperative reinforcement learning, Dung Nguyen, Svetha Venkatesh, Phuoc Nguyen, Truyen Tran, ACML'20.
• Learning to abstract and predict human actions, Romero Morais, Vuong Le, Truyen Tran, Svetha Venkatesh, BMVC'20.
• Object-centric relational reasoning for video question answering, Long Hoang Dang, Thao Minh Le, Vuong Le, Truyen Tran, The ECCV 2nd Workshop on Video Turing Test: Toward Human-Level Video Story Understanding, August 2020.
• Deep in the bowel: Highly interpretable neural encoder-decoder networks predict gut metabolites from gut microbiome, V Le, TP Quinn, T Tran, S Venkatesh, BMC Genomics (21), 07/2020.
• Self-attentive associative memory, Hung Le, Truyen Tran, Svetha Venkatesh, ICML'20.
• Dynamic language binding in relational visual reasoning, Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran, IJCAI'20, July 11-17, Yokohama, Japan.
  
• Neural reasoning, fast and slow, for video question answering, Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran, IJCNN'20
  
• Learning transferable domain priors for safe exploration in reinforcement learning, Thommen G Karimpanal, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh, IJCNN'20
  
• On catastrophic forgetting and mode collapse in Generative Adversarial Networks, Thanh-Tung, Hoang, and Truyen Tran, IJCNN'20
  
• Hierarchical conditional relation networks for video question answering, Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran, CVPR'20.
  
• Theory of mind with guilt aversion facilitates cooperative reinforcement learning, Dung Nguyen, Truyen Tran, Svetha Venkatesh, ICLR 2020 workshop on Bridging AI and Cognitive Science, April 26-30, Addis Ababa, Ethiopia.
  
• Neural stored-program memory, Hung Le, Truyen Tran, Svetha Venkatesh, ICLR'20.
  
• Theory and evaluation metrics for learning disentangled representations, K Do, T Tran, ICLR'20.
  
• DeepTRIAGE: Interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types, Adham Beykikhoshk, Thom P Quinn, Sam C Lee, Truyen Tran, Svetha Venkatesh, BMC Medical Genomics, 2020MC Medical Genomics, 2020
  
• Graph transformation policy network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
  
• Learning regularity in skeleton trajectories for anomaly detection in videos, Romero Morais, Vuong Le, Budhaditya Saha, Truyen Tran, Moussa Reda Mansour, Svetha Venkatesh, CVPR'19.
  
• Lessons learned from using a deep tree-based model for software defect prediction in practice, Hoa Khanh Dam, Trang Pham, Shien Wee Ng, Truyen Tran, John Grundy, Aditya Ghose, Taeksu Kim, Chul-Joo Kim, MSR'19.
  
• Learning to remember more with less memorization, Hung Le, Truyen Tran, Svetha Venkatesh, ICLR'19.
  
• Improving generalization and stability of Generative Adversarial Networks, Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh, ICLR'19.
  
• Incomplete conditional density estimation for fast materials discovery, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. SDM'19.
  
• Neural reasoning for chemical-chemical interaction. Trang Pham, Truyen Tran, Svetha Venkatesh, NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
  
• Attentional multilabel learning over graphs: A message passing approach, Kien Do, Truyen Tran, Thin Nguyen, SvethaVenkatesh, Machine Learning, 2019.
  
• Automatic feature learning for predicting vulnerable software components, Hoa Khanh Dam, Truyen Tran, Trang Pham,  Shien Wee Ng, John Grundy, Aditya Ghose, IEEE Transactions on Software Engineering, 2019.
  
• Variational memory encoder-decoder, Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh, NIPS'18.
  
• Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning, Hung Le, Truyen Tran, S vetha Venkatesh, KDD'18.
  
• On catastrophic forgetting and mode collapse in Generative Adversarial Networks, Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh; ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018.
  
• Graph Memory Networks for Molecular Activity Prediction, Trang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18. 
  
• Knowledge Graph Embedding with Multiple Relation Projections, Kien Do, Truyen Tran, Svetha Venkatesh, ICPR'18.
• Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records, Phuoc Nguyen, Truyen Tran, Svetha Venkatesh, IJCNN'18.
• Dual control memory augmented neural networks for treatment recommendations, Hung Le, Truyen Tran, Svetha Venkatesh, PAKDD'18.
• Predicting components for issue reports using deep learning with information retrieval, Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, International Conference on Software Engineering (ICSE'18) - Poster Track
• Energy-Based Anomaly Detection for Mixed Data, Kien Do, Truyen Tran, Svetha Venkatesh, Knowledge and Information Systems, 2018. Earlier works are:
• Multilevel Anomaly Detection for Mixed Data, Kien Do, Truyen Tran, Svetha Venkatesh, arXiv preprint arXiv: 1610.06249.
• Outlier Detection on Mixed-Type Data: An Energy-based Approach, Kien Do, Truyen Tran, Dinh Phung, Svetha Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016).
• A deep learning model for estimating story points, Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, Tim Menzies, IEEE Transactions on Software Engineering, 2018.
• Finding Algebraic Structure of Care in Time: A Deep Learning Approach, Phuoc Nguyen, Truyen Tran, Svetha Venkatesh, NIPS Workshop on Machine Learning for Health (ML4H).
• Graph Classification via Deep Learning with Virtual Nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh, Third Representation Learning for Graphs Workshop (ReLiG 2017).
• Deep Learning to Attend to Risk in ICU, Phuoc Nguyen, Truyen Tran, Svetha Venkatesh, IJCAI'17 Workshop on Knowledge Discovery in Healthcare II: Towards Learning Healthcare Systems (KDH 2017).
• Learning Recurrent Matrix Representation, Kien Do, Truyen Tran, Svetha Venkatesh. Third Representation Learning for Graphs Workshop (ReLiF 2017)
• Hierarchical semi-Markov conditional random fields for deep recursive sequential data, Truyen Tran, Dinh Phung, Hung Bui, Svetha Venkatesh,  Artificial Intelligence, Volume 246, May 2017, Pages 53–85. (Extension of the NIPS'08 paper).
• Predicting healthcare trajectories from medical records: A deep learning approach,Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, Journal of Biomedical Informatics, April 2017, DOI: 10.1016/j.jbi.2017.04.001. [Tech report PDF].
• Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh,  IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 22–30, Jan. 2017, Doi: 10.1109/JBHI.2016.2633963.
• Column Networks for Collective Classification, Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, AAAI'17
• Outlier Detection on Mixed-Type Data: An Energy-based Approach, Kien Do, Truyen Tran, Dinh Phung, Svetha Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016).
• Stabilizing Linear Prediction Models using Autoencoder, Shivapratap Gopakumara, Truyen Tran, Dinh Phung, Svetha Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016).
• A deep language model for software code, Hoa Khanh Dam, Truyen Tran and Trang Pham, FSE NL+SE 2016.
• DeepSoft: A vision for a deep model of software, Hoa Khanh Dam, Truyen Tran, John Grundy and Aditya Ghose, FSE VaR 2016.
• Faster Training of Very Deep Networks Via p-Norm Gates, Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, ICPR'16.
• DeepCare: A Deep Dynamic Memory Model for Predictive Medicine, Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, PAKDD'16, Auckland, NZ, April 2016. 
• Neural Choice by Elimination via Highway Networks, Truyen Tran, Dinh Phung and Svetha Venkatesh,  PAKDD workshop on Biologically Inspired Techniques for Data Mining (BDM'16), April 19-22 2016, Auckland, NZ.
• Graph-induced restricted Boltzmann machines for document modeling, Tu Dinh Nguyen, Truyen Tran, Dinh Phung, and Svetha Venkatesh, Information Sciences. doi:10.1016/j.ins.2015.08.023.
• Predicting delays in software projects using networked classification, Morakot Choetikertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, 30th IEEE/ACM International Conference on Automated Software Engineering, November 9–13, 2015 Lincoln, Nebraska, USA.
• Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (e-NRBM), Truyen Tran, Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh, Journal of Biomedical Informatics, 2015, pii: S1532-0464(15)00014-3. doi: 10.1016/j.jbi.2015.01.012. 
• Tensor-variate Restricted Boltzmann Machines, Tu Dinh Nguyen, Truyen Tran, Dinh Phung, and Svetha Venkatesh, AAAI 2015. 
• Thurstonian Boltzmann machines: Learning from multiple inequalities, Truyen Tran, Dinh Phung, and Svetha Venkatesh, In Proc. of 30th International Conference in Machine Learning (ICML’13), Atlanta, USA, June, 2013.
• Learning parts-based representations with Nonnegative Restricted Boltzmann Machine, Tu Dinh Nguyen, Truyen Tran, Dinh Phung, and Svetha Venkatesh, Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings, Vol. 29, Proc. of 5th Asian Conference on Machine Learning, Nov 2013.
• Latent patient profile modelling and applications with Mixed-Variate Restricted Boltzmann Machine, Tu Dinh Nguyen, Truyen Tran, Dinh Phung, and Svetha Venkatesh,  In Proc. of 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’13), Gold Coast, Australia, April 2013.
• Learning sparse latent representation and distance metric for image retrieval, Tu Dinh Nguyen, Truyen Tran, Dinh Phung, and Svetha Venkatesh, In Proc. of IEEE International Conference on Multimedia and Expo (ICME), San Jose, California, USA, July 2013.
• Learning from Ordered Sets and Applications in Collaborative Ranking, Truyen Tran, Dinh Phung and Svetha Venkatesh, in Proc. of. the 4th Asian Conference on Machine Learning (ACML2012), Singapore, Nov 2012.
• Cumulative Restricted Boltzmann Machines for Ordinal Data Analysis, Truyen Tran, Dinh Phung and Svetha Venkatesh, in Proc. of. the 4th Asian Conference on Machine Learning (ACML2012), Singapore, Nov 2012.
• Embedded Restricted Boltzmann Machines for Fusion of Mixed Data Types and Applications in Social Measurements Analysis, Truyen Tran, Dinh Phung, Svetha Venkatesh, in Proc. of 15-th International Conference on Information Fusion (FUSION-12), Singapore, July 2012.
• A Sequential Decision Approach to Ordinal Preferences in Recommender Systems, Truyen Tran, Dinh Phung, Svetha Venkatesh, in Proc. of 25-th Conference on Artificial Intelligence (AAAI-12), Toronto, Canada, July 2012.
• Learning Boltzmann Distance Metric for Face Recognition, Truyen Tran, Dinh Phung, Svetha Venkatesh, in Proc. of IEEE International Conference on Multimedia & Expo (ICME-12), Melbourne, Australia, July 2012.
• Mixed-Variate Restricted Boltzmann Machines, Truyen Tran, Dinh Phung and Svetha Venkatesh, in Proc. of. the 3rd Asian Conference on Machine Learning (ACML2011), Taoyuan, Taiwan, Nov 2011.
• Nonnegative Shared Subspace Learning and Its Application to Social Media Retrieval, Sunil Gupta, Dinh Phung, Brett. Adams, Tran The Truyen Proc. of 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 25-28 Jul, Washington DC, 2010. and Svetha Venkatesh, In
• Ordinal Boltzmann Machines for Collaborative Filtering. Truyen Tran, Dinh Q. Phung and Svetha Venkatesh. In Proc. of 25th Conference on Uncertainty in Artificial Intelligence, June, 2009, Montreal, Canada. Runner-up for the best paper award.
• MCMC for Hierarchical Semi-Markov Conditional Random Fields, Truyen Tran, Dinh Q. Phung, Svetha Venkatesh and Hung H. Bui. In NIPS'09 Workshop on Deep Learning for Speech Recognition and Related Applications. December, 2009, Whistler, BC, Canada
• Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data, Truyen Tran, Dinh Q. Phung, Hung H. Bui, and Svetha Venkatesh. In Proc. of 21st Annual Conference on Neural Information Processing Systems, Dec 2008, Vancouver, Canada. [See technical report and thesis for more details and extensions.]
• AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition, Truyen Tran, Dinh Quoc Phung, Hung Hai Bui, and Svetha Venkatesh. In Proc. of  IEEE Conference on Computer Vision and Pattern Recognition, volume Volume 2, pages 1686-1693, New York, USA, June 2006.