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AI Fundamentals

As an approach to general intelligence, we study new ways for differentiable learning to reason with minimal supervision, towards System 2 capability. Deep Learning achieves the goals through compositional neural networks, iterative estimation, and differentiable programming. Our research program draws certain inspiration from cognitive neuroscience, fused with rigorous probabilistic inference. The ultimate long-term goal is devise a unified cognitive architecture that guides the learning and reasoning across scales in space-time.

The research program has three broad aims:

Sub-area: New inductive biases

Successes in machine learning depend critically on having good priors on inductive biases. In deep learning, the strongest prior thus far has been neural architectures built on a small set of operators (signal filtering, convolution, recurrence, gating, memory and attention). We derive modular networks for regular data such as matrix and tensor as well as new data such as graphs and relations. We draw our architectural inspiration from neuroscience including the columnar structure of the neocortex for distributed processing, the thalamus structure for information routing, working memory for problem solving, and episodic memory for integrating information over time.

Column Networks, as inspired by the cortical columns, to solve multi-relational learning.

Sub-area: Memory-enabled AI

Deep neural networks excel at function approximation and pattern recognition but fall short on manipulating complex, highly dependent systems, rapid contextualisation in new settings, retaining previously acquired skills, and holding long conversations. These limitations are possibly due to the lack of an explicit notion of memory. We design new kinds of memory with more robust handling of variability, less memorization, and stored programs. The memory serves as a central component in a grand unified cognitive architecture that naturally supports learning, reasoning, rapid contextualisation and imagination.

Variational Memory Encoder-Decoder, as applied for generating a diverse and coherent dialog.

Sub-area: Learning to reason

We are concerned about learning the capability to deduce new knowledge from previously acquired knowledge in response to a query. Such behaviours can be demonstrated naturally using a symbolic system with a rich set inferential tools, given that the symbols can be grounded to 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, e.g., answering new questions over space-time in a compositional and progressive fashion.

A system for Video Question Answering that implements the dual-process theory of reasoning.

Sub-area: Abstraction and analogy

A system for abstracting out visual details, focusing on relations between images via an indirection mechanism. This is capable of solving IQ problems.

Sub-area: Learning with less labels

Learning with a few explicit labels is the hallmark of human intelligence. Leveraging unlabelled data, either through existing datasets, or through self-exploration, will be critical to the next AI generation. We investigate the following sub-areas. Representation learning: Learning starts with representation of latent factors in the data which are invariant to small changes and insensitive of noise. Generative models: The ability to model high-dimensional world and to imagine the future is fundamental to AI. We investigate fundamental issues of deep generative models including stability, generalisation and catastrophic forgetting in Generative Adversarial Networks, as well as disentanglement in Variational Auto-Encoders. Continual learning: We design new learning algorithms that adapt continually as new tasks are introduced, even if the task change is not explicitly marked.

A Boltzmann machine for recommender system.

Sub-area: Social reinforcement learning

We leverage deep neural networks to enable multi-agent systems to perceive the world, act on it, interact with others, build theory of mind, imagine the future and receive feedbacks. Equipped with deep nets for perception, memory, statistical relational learning, and reasoning capabilities, we aim to bring multi-agent reinforcement learning to a new level.

A system of multi-agents equipped with social psychology.

Sub-area:    Human-compatible learning

The rapid advancement of AI raises new ethical challenges which pose great risks to humanity if unsolved. We aim to invent new machine learning algorithms that teach machine to be compatible with human preferences. We derive  computational frameworks for instilling intrinsic human preferences into AI through preference learning, alignment optimisation, and preference-guided agent designs.

Ongoing Projects:

·       Human behaviour understanding in video

This project aims at a deep understanding of human behaviours seen through (fixed and moving) videos in various indoor and outdoor contexts. We build new models of trajectories and social interactions, inferring past trigger events, and predict actions and intention.

Partners: iCetana

Detecting anomalies in video using skeleton trajectories (last row).

·       Visual question answering and dialog

We study the new cognitive capability of a system to answer new natural questions about an image or a video. This is a powerful way to demonstrate the reasoning capacity, which involves linguistic, visual processing and high-level symbols manipulation skills. In visual dialog, we build a system having a natural multi-turn chat with human about a visual object.

Answering questions about a video.

·       AI for structural biology

This research aims at designing neural architectures for representation of -omics and structured biological data. We map genotype-phenotype, answer any genomic queries for a given sequence, predict protein-target interactions, estimate protein folding, design drugs, and learn to generate DNA/RNA and protein. The long-term goals also include acquiring, organizing and reasoning about established biology knowledge.

Partners: TBA.

·       Exploring the molecular and materials space

We use deep learning to characterise the chemical space, replace expensive physical computation and experiments, predict molecular properties, molecular-molecular interactions and chemical reactions, and generate drug molecules given a set of desirable bioactivity properties. In materials design, we design new tools for understanding the structure and characteristics of materials, searching for new alloys, and generating molecules & crystals.

Partners: Institute of Frontier Materials at Deakin, Japan Institute of Advanced Science and Technology.

Relational Dynamic Memory Network, a model for detecting interactions among molecules.

• 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

• 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.