no code implementations • 18 Apr 2024 • Hung Le, Dung Nguyen, Kien Do, Svetha Venkatesh, Truyen Tran
We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing to new, longer sequences of data.
no code implementations • 5 Feb 2024 • Kien Do, Dung Nguyen, Hung Le, Thao Le, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh
To overcome this challenge, we propose to approximate \frac{1}{p(u|b)} using a biased classifier trained with "bias amplification" losses.
no code implementations • 19 Dec 2023 • Phuoc Nguyen, Truyen Tran, Sunil Gupta, Thin Nguyen, Svetha Venkatesh
We then represent the functional form of a target outlier leaf as a function of the node and edge noises.
1 code implementation • 21 Aug 2023 • Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana, Hung Le, Truyen Tran, Sunil Gupta, Svetha Venkatesh
To address this issue, we introduce SEQ (sample efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions.
no code implementations • ICCV 2023 • Hung Tran, Vuong Le, Svetha Venkatesh, Truyen Tran
To bridge that gap, this work proposes to model two concurrent mechanisms that jointly control human motion: the Persistent process that runs continually on the global scale, and the Transient sub-processes that operate intermittently on the local context of the human while interacting with objects.
no code implementations • 17 Jan 2023 • Dung Nguyen, Phuoc Nguyen, Hung Le, Kien Do, Svetha Venkatesh, Truyen Tran
Social reasoning necessitates the capacity of theory of mind (ToM), the ability to contextualise and attribute mental states to others without having access to their internal cognitive structure.
no code implementations • 23 Oct 2022 • Kha Pham, Hung Le, Man Ngo, Truyen Tran
FINE consists of a backbone network and a trainable semantic memory of basis weight matrices.
no code implementations • 21 Sep 2022 • Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh
Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator.
1 code implementation • 8 Jul 2022 • Hoang-Anh Pham, Thao Minh Le, Vuong Le, Tu Minh Phuong, Truyen Tran
To tackle these challenges we present a new object-centric framework for video dialog that supports neural reasoning dubbed COST - which stands for Conversation about Objects in Space-Time.
no code implementations • 25 May 2022 • Thao Minh Le, Vuong Le, Sunil Gupta, Svetha Venkatesh, Truyen Tran
This grounding guides the attention mechanism inside VQA models through a duality of mechanisms: pre-training attention weight calculation and directly guiding the weights at inference time on a case-by-case basis.
no code implementations • 13 May 2022 • Phuoc Nguyen, Truyen Tran, Ky Le, Sunil Gupta, Santu Rana, Dang Nguyen, Trong Nguyen, Shannon Ryan, Svetha Venkatesh
We introduce a conditional compression problem and propose a fast framework for tackling it.
no code implementations • 21 Apr 2022 • Hung Tran, Vuong Le, Svetha Venkatesh, Truyen Tran
We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions.
no code implementations • 17 Apr 2022 • Dung Nguyen, Phuoc Nguyen, Hung Le, Kien Do, Svetha Venkatesh, Truyen Tran
Inspired by the observation that humans often infer the character traits of others, then use it to explain behaviour, we propose a new neural ToM architecture that learns to generate a latent trait vector of an actor from the past trajectories.
no code implementations • 17 Apr 2022 • Dung Nguyen, Phuoc Nguyen, Svetha Venkatesh, Truyen Tran
In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment.
no code implementations • 24 Feb 2022 • Kien Do, Haripriya Harikumar, Hung Le, Dung Nguyen, Truyen Tran, Santu Rana, Dang Nguyen, Willy Susilo, Svetha Venkatesh
Trojan attacks on deep neural networks are both dangerous and surreptitious.
no code implementations • 14 Feb 2022 • Tri Minh Nguyen, Thin Nguyen, Truyen Tran
Discovering new medicines is the hallmark of human endeavor to live a better and longer life.
1 code implementation • 16 Jan 2022 • Tri Minh Nguyen, Thin Nguyen, Truyen Tran
While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction.
no code implementations • 3 Nov 2021 • Thommen George Karimpanal, Hung Le, Majid Abdolshah, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard $Q-$learning.
no code implementations • NeurIPS 2021 • Hung Le, Thommen Karimpanal George, Majid Abdolshah, Truyen Tran, Svetha Venkatesh
Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory.
no code implementations • ICLR 2022 • Kha Pham, Hung Le, Man Ngo, Truyen Tran, Bao Ho, Svetha Venkatesh
We propose Generative Pseudo-Inverse Memory (GPM), a class of deep generative memory models that are fast to write in and read out.
no code implementations • 29 Sep 2021 • Thommen Karimpanal George, Majid Abdolshah, Hung Le, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh
The objective in goal-based reinforcement learning is to learn a policy to reach a particular goal state within the environment.
no code implementations • ICCV 2021 • Kien Do, Truyen Tran, Svetha Venkatesh
We propose a novel framework for image clustering that incorporates joint representation learning and clustering.
no code implementations • 25 Jun 2021 • Long Hoang Dang, Thao Minh Le, Vuong Le, Truyen Tran
Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects.
1 code implementation • 20 May 2021 • Binh Nguyen-Thai, Vuong Le, Catherine Morgan, Nadia Badawi, Truyen Tran, Svetha Venkatesh
The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants.
no code implementations • 12 Apr 2021 • Long Hoang Dang, Thao Minh Le, Vuong Le, Truyen Tran
Video question answering (Video QA) presents a powerful testbed for human-like intelligent behaviors.
1 code implementation • 24 Mar 2021 • Tri Minh Nguyen, Thomas P Quinn, Thin Nguyen, Truyen Tran
Methods: We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex.
no code implementations • CVPR 2021 • Romero Morais, Vuong Le, Svetha Venkatesh, Truyen Tran
Their interactions are sparse in time hence more faithful to the true underlying nature and more robust in inference and learning.
1 code implementation • 3 Dec 2020 • Kien Do, Truyen Tran, Svetha Venkatesh
We propose two generic methods for improving semi-supervised learning (SSL).
no code implementations • 19 Nov 2020 • Anh-Cat Le-Ngo, Truyen Tran, Santu Rana, Sunil Gupta, Svetha Venkatesh
We propose a new model-agnostic logic constraint to tackle this issue by formulating a logically consistent loss in the multi-task learning framework as well as a data organisation called family-batch and hybrid-batch.
no code implementations • 5 Nov 2020 • Hung Tran, Vuong Le, Truyen Tran
We design Goal-driven Trajectory Prediction model - a dual-channel neural network that realizes such intuition.
1 code implementation • NeurIPS Workshop ICBINB 2020 • Hoang Thanh-Tung, Truyen Tran
In this paper, we investigate the capacity of these metrics in measuring the generalization capacity.
no code implementations • 18 Oct 2020 • Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran
Video QA challenges modelers in multiple fronts.
1 code implementation • 25 Sep 2020 • Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, Truyen Tran
In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets.
no code implementations • 20 Sep 2020 • Duc Nguyen, Phuoc Nguyen, Kien Do, Santu Rana, Sunil Gupta, Truyen Tran
These include the capacity of the compact matrix LSTM to compress noisy data near perfectly, making the strategy of compressing-decompressing data ill-suited for anomaly detection under the noise.
no code implementations • 16 Sep 2020 • Dung Nguyen, Svetha Venkatesh, Phuoc Nguyen, Truyen Tran
In psychological game theory, guilt aversion necessitates modelling of agents that have theory about what other agents think, also known as Theory of Mind (ToM).
1 code implementation • 20 Aug 2020 • Romero Morais, Vuong Le, Truyen Tran, Svetha Venkatesh
We propose Hierarchical Encoder-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction.
no code implementations • 18 May 2020 • Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Hieu-Chi Dam, Svetha Venkatesh
Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(\theta).
1 code implementation • 30 Apr 2020 • Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran
We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering.
1 code implementation • CVPR 2020 • Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran
Video question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts.
Ranked #3 on Audio-Visual Question Answering (AVQA) on AVQA
Audio-Visual Question Answering (AVQA) Question Answering +4
1 code implementation • ICML 2020 • Hung Le, Truyen Tran, Svetha Venkatesh
Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions.
Ranked #1 on Question Answering on bAbi
no code implementations • 10 Sep 2019 • Thommen George Karimpanal, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh
Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent.
2 code implementations • ICLR 2020 • Kien Do, Truyen Tran
We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation.
no code implementations • 10 Jul 2019 • Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran
While recent advances in lingual and visual question answering have enabled sophisticated representations and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process.
1 code implementation • ICLR 2020 • Hung Le, Truyen Tran, Svetha Venkatesh
Neural networks powered with external memory simulate computer behaviors.
Ranked #5 on Question Answering on bAbi (Mean Error Rate metric)
1 code implementation • CVPR 2019 • Romero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour, Svetha Venkatesh
Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors.
Ranked #6 on Video Anomaly Detection on HR-ShanghaiTech
1 code implementation • ICLR 2019 • Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh
We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator.
1 code implementation • ICLR 2019 • Hung Le, Truyen Tran, Svetha Venkatesh
Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning.
Ranked #5 on Text Classification on Yahoo! Answers
no code implementations • 27 Dec 2018 • Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose, Yasutaka Kamei
The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management.
no code implementations • 22 Dec 2018 • Kien Do, Truyen Tran, Svetha Venkatesh
We address a fundamental problem in chemistry known as chemical reaction product prediction.
no code implementations • 10 Aug 2018 • Trang Pham, Truyen Tran, Svetha Venkatesh
Neural networks excel in detecting regular patterns but are less successful in representing and manipulating complex data structures, possibly due to the lack of an external memory.
1 code implementation • NeurIPS 2018 • Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh
Introducing variability while maintaining coherence is a core task in learning to generate utterances in conversation.
1 code implementation • 11 Jul 2018 • Hoang Thanh-Tung, Truyen Tran
We show that GAN training is a continual learning problem in which the sequence of changing model distributions is the sequence of tasks to the discriminator.
no code implementations • 1 Apr 2018 • Kien Do, Truyen Tran, Thin Nguyen, Svetha Venkatesh
GAML regards labels as auxiliary nodes and models them in conjunction with the input graph.
no code implementations • 11 Feb 2018 • Hung Le, Truyen Tran, Svetha Venkatesh
The decoding controller generates a treatment sequence, one treatment option at a time.
1 code implementation • 3 Feb 2018 • Asjad Khan, Hung Le, Kien Do, Truyen Tran, Aditya Ghose, Hoa Dam, Renuka Sindhgatta
Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next.
no code implementations • 3 Feb 2018 • Phuoc Nguyen, Truyen Tran, Svetha Venkatesh
The same hold for the bag of treatments.
1 code implementation • 3 Feb 2018 • Hoa Khanh Dam, Trang Pham, Shien Wee Ng, Truyen Tran, John Grundy, Aditya Ghose, Taeksu Kim, Chul-Joo Kim
Defects are common in software systems and can potentially cause various problems to software users.
Software Engineering
1 code implementation • 2 Feb 2018 • Hung Le, Truyen Tran, Svetha Venkatesh
One of the core tasks in multi-view learning is to capture relations among views.
no code implementations • 26 Jan 2018 • Kien Do, Truyen Tran, Svetha Venkatesh
Knowledge graphs contain rich relational structures of the world, and thus complement data-driven machine learning in heterogeneous data.
no code implementations • 8 Jan 2018 • Trang Pham, Truyen Tran, Svetha Venkatesh
GraphMem is capable of jointly training on multiple datasets by using a specific-task query fed to the controller as an input.
no code implementations • 21 Nov 2017 • Phuoc Nguyen, Truyen Tran, Svetha Venkatesh
The interaction between diseases and treatments at a visit is modeled as the residual of the diseases minus the treatments.
no code implementations • 18 Aug 2017 • Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh
Of current representation learning schemes, restricted Boltzmann machines (RBMs) have proved to be highly effective in unsupervised settings.
no code implementations • 18 Aug 2017 • Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh
The analysis of mixed data has been raising challenges in statistics and machine learning.
no code implementations • 14 Aug 2017 • Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh
The representation of the virtual node is then the representation of the entire graph.
no code implementations • 8 Aug 2017 • Hoa Khanh Dam, Truyen Tran, Trang Pham, Shien Wee Ng, John Grundy, Aditya Ghose
Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a variety of problems including deadlock, information loss, or system failure.
Software Engineering
no code implementations • 17 Jul 2017 • Phuoc Nguyen, Truyen Tran, Svetha Venkatesh
At the reasoning layer, evidences across time steps are weighted and combined.
no code implementations • 4 Mar 2017 • Kien Do, Truyen Tran, Svetha Venkatesh
We derive several new deep networks: (i) feed-forward nets that map an input matrix into an output matrix, (ii) recurrent nets which map a sequence of input matrices into a sequence of output matrices.
no code implementations • 22 Feb 2017 • Trang Pham, Truyen Tran, Svetha Venkatesh
Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning.
no code implementations • 20 Oct 2016 • Kien Do, Truyen Tran, Svetha Venkatesh
We propose MIXMAD, which stands for MIXed data Multilevel Anomaly Detection, an ensemble method that estimates the sparse regions across multiple levels of abstraction of mixed data.
no code implementations • 28 Sep 2016 • Shivapratap Gopakumar, Truyen Tran, Dinh Phung, Svetha Venkatesh
Using a linear model as basis for prediction, we achieve feature stability by regularising latent correlation in features.
1 code implementation • 15 Sep 2016 • Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh
CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) long-range, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations.
no code implementations • 2 Sep 2016 • Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, Tim Menzies
Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues.
1 code implementation • 17 Aug 2016 • Kien Do, Truyen Tran, Dinh Phung, Svetha Venkatesh
We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of Mv. RBM is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts.
no code implementations • 11 Aug 2016 • Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh
Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforward models such as Residual Nets and Highway Networks.
1 code implementation • 9 Aug 2016 • Hoa Khanh Dam, Truyen Tran, Trang Pham
Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart.
no code implementations • 30 Jul 2016 • Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose
Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption.
no code implementations • 28 Jul 2016 • Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh
Preterm births occur at an alarming rate of 10-15%.
no code implementations • 26 Jul 2016 • Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh
On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk.
no code implementations • 3 May 2016 • Thuong Nguyen, Truyen Tran, Shivapratap Gopakumar, Dinh Phung, Svetha Venkatesh
Accurate prediction of suicide risk in mental health patients remains an open problem.
no code implementations • 4 Mar 2016 • Truyen Tran, Dinh Phung, Svetha Venkatesh
We introduce a deep multitask architecture to integrate multityped representations of multimodal objects.
no code implementations • 17 Feb 2016 • Truyen Tran, Dinh Phung, Svetha Venkatesh
We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank.
no code implementations • 9 Feb 2016 • Truyen Tran, Dinh Phung, Svetha Venkatesh
Recommender systems play a central role in providing individualized access to information and services.
1 code implementation • 1 Feb 2016 • Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh
We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes.
no code implementations • 6 Aug 2014 • Truyen Tran, Hung Bui, Svetha Venkatesh
We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition.
no code implementations • 6 Aug 2014 • Truyen Tran, Hung Bui, Svetha Venkatesh
Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance.
no code implementations • 6 Aug 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh
Modern datasets are becoming heterogeneous.
no code implementations • 6 Aug 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh, Hung H. Bui
In this contribution, we propose a new approximation technique that may have the potential to achieve sub-cubic time complexity in length and linear time depth, at the cost of some loss of quality.
no code implementations • 1 Aug 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time.
no code implementations • 31 Jul 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc.
no code implementations • 31 Jul 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh
Ranking over sets arise when users choose between groups of items.
no code implementations • 24 Jul 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh
Learning structured outputs with general structures is computationally challenging, except for tree-structured models.
no code implementations • 23 Jul 2014 • Shivapratap Gopakumar, Truyen Tran, Dinh Phung, Svetha Venkatesh
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention.
no code implementations • 23 Jul 2014 • Truyen Tran, Svetha Venkatesh
Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks.
no code implementations • 23 Jul 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh
In practical settings, the task often reduces to estimating a rank functional of an object with respect to a query.
no code implementations • 22 Jul 2014 • Truyen Tran, Trung Thanh Nguyen, Hoang Linh Nguyen
This paper studies a class of enhanced diffusion processes in which random walkers perform L\'evy flights and apply it for global optimization.
no code implementations • 22 Jul 2014 • Truyen Tran, Dinh Phung, Svetha Venkatesh
The \emph{maximum a posteriori} (MAP) assignment for general structure Markov random fields (MRFs) is computationally intractable.