Search Results for author: Dinh Phung

Found 132 papers, 51 papers with code

Parameterized Rate-Distortion Stochastic Encoder

no code implementations ICML 2020 Quan Hoang, Trung Le, Dinh Phung

We propose a novel gradient-based tractable approach for the Blahut-Arimoto (BA) algorithm to compute the rate-distortion function where the BA algorithm is fully parameterized.

Frequency Attention for Knowledge Distillation

1 code implementation9 Mar 2024 Cuong Pham, Van-Anh Nguyen, Trung Le, Dinh Phung, Gustavo Carneiro, Thanh-Toan Do

Inspired by the benefits of the frequency domain, we propose a novel module that functions as an attention mechanism in the frequency domain.

Image Classification Knowledge Distillation +3

Optimal Transport for Structure Learning Under Missing Data

1 code implementation23 Feb 2024 Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung

Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirical shown to be sub-optimal.

Causal Discovery Imputation

Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs

no code implementations17 Feb 2024 Minh-Vuong Nguyen, Linhao Luo, Fatemeh Shiri, Dinh Phung, Yuan-Fang Li, Thuy-Trang Vu, Gholamreza Haffari

Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.

Knowledge Graphs Multi-hop Question Answering +1

A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation

no code implementations29 Jan 2024 Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, Dinh Phung

Additionally, we propose minimizing class-aware Higher-order Moment Matching (HMM) to align the corresponding class regions on the source and target domains.

Unsupervised Domain Adaptation

Class-Prototype Conditional Diffusion Model for Continual Learning with Generative Replay

no code implementations10 Dec 2023 Khanh Doan, Quyen Tran, Tuan Nguyen, Dinh Phung, Trung Le

To address this, we propose the Class-Prototype Conditional Diffusion Model (CPDM), a GR-based approach for continual learning that enhances image quality in generators and thus reduces catastrophic forgetting in classifiers.

Continual Learning Denoising +1

KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All

no code implementations26 Nov 2023 Quyen Tran, Lam Tran, Khoat Than, Toan Tran, Dinh Phung, Trung Le

Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field of Continual Learning.

Continual Learning Meta-Learning

Robust Contrastive Learning With Theory Guarantee

no code implementations16 Nov 2023 Ngoc N. Tran, Lam Tran, Hoang Phan, Anh Bui, Tung Pham, Toan Tran, Dinh Phung, Trung Le

Contrastive learning (CL) is a self-supervised training paradigm that allows us to extract meaningful features without any label information.

Contrastive Learning

PhoGPT: Generative Pre-training for Vietnamese

1 code implementation6 Nov 2023 Dat Quoc Nguyen, Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, Dinh Phung, Hung Bui

The base model, PhoGPT-4B, with exactly 3. 7B parameters, is pre-trained from scratch on a Vietnamese corpus of 102B tokens, with an 8192 context length, employing a vocabulary of 20480 token types.

Instruction Following

Cross-adversarial local distribution regularization for semi-supervised medical image segmentation

no code implementations2 Oct 2023 Thanh Nguyen-Duc, Trung Le, Roland Bammer, He Zhao, Jianfei Cai, Dinh Phung

Medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data.

Image Segmentation Segmentation +2

Unleash Data Generation for Efficient and Effective Data-free Knowledge Distillation

no code implementations30 Sep 2023 Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Quan Hung Tran, Dinh Phung

By reinitializing the noisy layer in each iteration, we aim to facilitate the generation of diverse samples while still retaining the method's efficiency, thanks to the ease of learning provided by LTE.

Data-free Knowledge Distillation

Towards Generalising Neural Topical Representations

no code implementations24 Jul 2023 Xiaohao Yang, He Zhao, Dinh Phung, Lan Du

Although NTMs have achieved promising performance when trained and tested on a specific corpus, their generalisation ability across corpora is rarely studied.

Data Augmentation Topic Models

Optimal Transport Model Distributional Robustness

1 code implementation NeurIPS 2023 Van-Anh Nguyen, Trung Le, Anh Tuan Bui, Thanh-Toan Do, Dinh Phung

Interestingly, our developed theories allow us to flexibly incorporate the concept of sharpness awareness into training, whether it's a single model, ensemble models, or Bayesian Neural Networks, by considering specific forms of the center model distribution.

Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities

1 code implementation26 May 2023 Michael Fu, Trung Le, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Phung

Prior studies found that vulnerabilities across different vulnerable programs may exhibit similar vulnerable scopes, implicitly forming discernible vulnerability patterns that can be learned by DL models through supervised training.

Vulnerability Detection

Learning Directed Graphical Models with Optimal Transport

1 code implementation25 May 2023 Vy Vo, Trung Le, Long-Tung Vuong, He Zhao, Edwin Bonilla, Dinh Phung

Estimating the parameters of a probabilistic directed graphical model from incomplete data remains a long-standing challenge.

Representation Learning

Sharpness & Shift-Aware Self-Supervised Learning

no code implementations17 May 2023 Ngoc N. Tran, Son Duong, Hoang Phan, Tung Pham, Dinh Phung, Trung Le

Self-supervised learning aims to extract meaningful features from unlabeled data for further downstream tasks.

Classification Contrastive Learning +2

Active Continual Learning: On Balancing Knowledge Retention and Learnability

no code implementations6 May 2023 Thuy-Trang Vu, Shahram Khadivi, Mahsa Ghorbanali, Dinh Phung, Gholamreza Haffari

Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL).

Active Learning Continual Learning +1

Generating Adversarial Examples with Task Oriented Multi-Objective Optimization

1 code implementation26 Apr 2023 Anh Bui, Trung Le, He Zhao, Quan Tran, Paul Montague, Dinh Phung

The key factor for the success of adversarial training is the capability to generate qualified and divergent adversarial examples which satisfy some objectives/goals (e. g., finding adversarial examples that maximize the model losses for simultaneously attacking multiple models).

Hyperbolic Geometry in Computer Vision: A Survey

no code implementations21 Apr 2023 Pengfei Fang, Mehrtash Harandi, Trung Le, Dinh Phung

Hyperbolic geometry, a Riemannian manifold endowed with constant sectional negative curvature, has been considered an alternative embedding space in many learning scenarios, \eg, natural language processing, graph learning, \etc, as a result of its intriguing property of encoding the data's hierarchical structure (like irregular graph or tree-likeness data).

Graph Learning Image Classification

Vector Quantized Wasserstein Auto-Encoder

no code implementations12 Feb 2023 Tung-Long Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Phung

Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks.

Clustering Image Reconstruction

Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance

no code implementations5 Dec 2022 Ngoc N. Tran, Anh Tuan Bui, Dinh Phung, Trung Le

On the other hand, in order to achieve that, we need to devise even stronger adversarial attacks to challenge these defense models.

Continual Learning with Optimal Transport based Mixture Model

no code implementations30 Nov 2022 Quyen Tran, Hoang Phan, Khoat Than, Dinh Phung, Trung Le

To address this issue, in this work, we first propose an online mixture model learning approach based on nice properties of the mature optimal transport theory (OT-MM).

Class Incremental Learning Incremental Learning

Improving Multi-task Learning via Seeking Task-based Flat Regions

no code implementations24 Nov 2022 Hoang Phan, Lam Tran, Ngoc N. Tran, Nhat Ho, Dinh Phung, Trung Le

Multi-Task Learning (MTL) is a widely-used and powerful learning paradigm for training deep neural networks that allows learning more than one objective by a single backbone.

Multi-Task Learning speech-recognition +1

Vision Transformer Visualization: What Neurons Tell and How Neurons Behave?

1 code implementation14 Oct 2022 Van-Anh Nguyen, Khanh Pham Dinh, Long Tung Vuong, Thanh-Toan Do, Quan Hung Tran, Dinh Phung, Trung Le

Our approach departs from the computational process of ViTs with a focus on visualizing the local and global information in input images and the latent feature embeddings at multiple levels.

Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations

1 code implementation27 Sep 2022 Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin Bonilla, Gholamreza Haffari, Dinh Phung

Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability.

counterfactual feature selection +3

Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle

1 code implementation19 Sep 2022 Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John Grundy, Hung Nguyen, Dinh Phung

However, there are still two open and significant issues for SVD in terms of i) learning automatic representations to improve the predictive performance of SVD, and ii) tackling the scarcity of labeled vulnerabilities datasets that conventionally need laborious labeling effort by experts.

Domain Adaptation Representation Learning +2

MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation

2 code implementations19 Sep 2022 Chuanxia Zheng, Long Tung Vuong, Jianfei Cai, Dinh Phung

Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches within an image into the same index, resulting in a repeated artifact for similar adjacent regions using existing decoder architectures.

Image Generation Quantization +1

An Additive Instance-Wise Approach to Multi-class Model Interpretation

1 code implementation7 Jul 2022 Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Phung

A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner.

Additive models Interpretable Machine Learning

Stochastic Multiple Target Sampling Gradient Descent

1 code implementation4 Jun 2022 Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung

Furthermore, when analysing its asymptotic properties, SVGD reduces exactly to a single-objective optimization problem and can be viewed as a probabilistic version of this single-objective optimization problem.

Multi-Task Learning

High-Quality Pluralistic Image Completion via Code Shared VQGAN

no code implementations5 Apr 2022 Chuanxia Zheng, Guoxian Song, Tat-Jen Cham, Jianfei Cai, Dinh Phung, Linjie Luo

In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed.

Image Reconstruction Vocal Bursts Intensity Prediction

Global-Local Regularization Via Distributional Robustness

1 code implementation1 Mar 2022 Hoang Phan, Trung Le, Trung Phung, Tuan Anh Bui, Nhat Ho, Dinh Phung

First, they purely focus on local regularization to strengthen model robustness, missing a global regularization effect which is useful in many real-world applications (e. g., domain adaptation, domain generalization, and adversarial machine learning).

Domain Generalization

A Unified Wasserstein Distributional Robustness Framework for Adversarial Training

1 code implementation ICLR 2022 Tuan Anh Bui, Trung Le, Quan Tran, He Zhao, Dinh Phung

We introduce a new Wasserstein cost function and a new series of risk functions, with which we show that standard AT methods are special cases of their counterparts in our framework.

Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics

1 code implementation22 Feb 2022 Tam Le, Truyen Nguyen, Dinh Phung, Viet Anh Nguyen

In this work, we consider probability measures supported on a graph metric space and propose a novel Sobolev transport metric.

Document Classification Topological Data Analysis +1

Two-view Graph Neural Networks for Knowledge Graph Completion

1 code implementation16 Dec 2021 Vinh Tong, Dai Quoc Nguyen, Dinh Phung, Dat Quoc Nguyen

WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes.

Knowledge Graph Completion Knowledge Graph Embedding +2

On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources

no code implementations NeurIPS 2021 Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Phung

Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e. g., learning domain-invariant representations and its trade-off.

Domain Generalization Transfer Learning

On Label Shift in Domain Adaptation via Wasserstein Distance

no code implementations29 Oct 2021 Trung Le, Dat Do, Tuan Nguyen, Huy Nguyen, Hung Bui, Nhat Ho, Dinh Phung

We study the label shift problem between the source and target domains in general domain adaptation (DA) settings.

Domain Adaptation

On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks

no code implementations29 Oct 2021 Dang Nguyen, Trang Nguyen, Khai Nguyen, Dinh Phung, Hung Bui, Nhat Ho

To address this issue, we propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers, which we refer to as heterogeneous neural networks, via cross-layer alignment.

Knowledge Distillation Model Compression

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

1 code implementation14 Oct 2021 Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, Dinh Phung

Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks.

Graph Embedding text-classification +2

STEM: An Approach to Multi-Source Domain Adaptation With Guarantees

1 code implementation1 Oct 2021 Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung

To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.

Fine-grained Software Vulnerability Detection via Information Theory and Contrastive Learning

no code implementations29 Sep 2021 Van Nguyen, Trung Le, John C. Grundy, Dinh Phung

Software vulnerabilities existing in a program or function of computer systems have been becoming a serious and crucial concern.

Contrastive Learning Representation Learning +1

Improving Robustness with Optimal Transport based Adversarial Generalization

no code implementations29 Sep 2021 Siqi Xia, Shijie Liu, Trung Le, Dinh Phung, Sarah Erfani, Benjamin I. P. Rubinstein, Christopher Leckie, Paul Montague

More specifically, by minimizing the WS distance of interest, an adversarial example is pushed toward the cluster of benign examples sharing the same label on the latent space, which helps to strengthen the generalization ability of the classifier on the adversarial examples.

Contrastively Enforcing Distinctiveness for Multi-Label Classification

no code implementations29 Sep 2021 Son Duy Dao, He Zhao, Dinh Phung, Jianfei Cai

Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains.

Classification Contrastive Learning +2

LASSO: Latent Sub-spaces Orientation for Domain Generalization

no code implementations29 Sep 2021 Long Tung Vuong, Trung Quoc Phung, Toan Tran, Anh Tuan Tran, Dinh Phung, Trung Le

To achieve a satisfactory generalization performance on prediction tasks in an unseen domain, existing domain generalization (DG) approaches often rely on the strict assumption of fixed domain-invariant features and common hypotheses learned from a set of training domains.

Domain Generalization

Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection

1 code implementation EMNLP 2021 Thuy-Trang Vu, Xuanli He, Dinh Phung, Gholamreza Haffari

Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.

Contrastive Learning Machine Translation +3

Multi-Label Image Classification with Contrastive Learning

no code implementations24 Jul 2021 Son D. Dao, Ethan Zhao, Dinh Phung, Jianfei Cai

Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains.

Classification Contrastive Learning +2

Improved and Efficient Text Adversarial Attacks using Target Information

no code implementations27 Apr 2021 Mahmoud Hossam, Trung Le, He Zhao, Viet Huynh, Dinh Phung

There has been recently a growing interest in studying adversarial examples on natural language models in the black-box setting.

Sentence

Text Generation with Deep Variational GAN

no code implementations27 Apr 2021 Mahmoud Hossam, Trung Le, Michael Papasimeon, Viet Huynh, Dinh Phung

Generating realistic sequences is a central task in many machine learning applications.

Text Generation

Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction

1 code implementation15 Apr 2021 Dai Quoc Nguyen, Vinh Tong, Dinh Phung, Dat Quoc Nguyen

We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i. e., link prediction).

Knowledge Graph Completion Link Prediction

Topic Modelling Meets Deep Neural Networks: A Survey

no code implementations28 Feb 2021 He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray Buntine

Topic modelling has been a successful technique for text analysis for almost twenty years.

Navigate Text Generation +1

On Transportation of Mini-batches: A Hierarchical Approach

2 code implementations11 Feb 2021 Khai Nguyen, Dang Nguyen, Quoc Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho

To address these problems, we propose a novel mini-batch scheme for optimal transport, named Batch of Mini-batches Optimal Transport (BoMb-OT), that finds the optimal coupling between mini-batches and it can be seen as an approximation to a well-defined distance on the space of probability measures.

Domain Adaptation

Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning

1 code implementation25 Jan 2021 Anh Bui, Trung Le, He Zhao, Paul Montague, Seyit Camtepe, Dinh Phung

Central to this approach is the selection of positive (similar) and negative (dissimilar) sets to provide the model the opportunity to `contrast' between data and class representation in the latent space.

Contrastive Learning

STEM: An Approach to Multi-Source Domain Adaptation With Guarantees

1 code implementation ICCV 2021 Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung

To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.

Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering

no code implementations COLING 2020 Quan Tran, Nhan Dam, Tuan Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung

Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests.

Question Answering

Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks

no code implementations13 Oct 2020 He Zhao, Thanh Nguyen, Trung Le, Paul Montague, Olivier De Vel, Tamas Abraham, Dinh Phung

Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.

Adversarial Attack Detection

Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models

1 code implementation EMNLP 2020 Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari

Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.

named-entity-recognition Named Entity Recognition +2

QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings

1 code implementation26 Sep 2020 Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Phung

We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs.

Knowledge Graph Embeddings Relation

Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness

1 code implementation21 Sep 2020 Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham, Dinh Phung

An important technique of this approach is to control the transferability of adversarial examples among ensemble members.

Adversarial Robustness

Neural Topic Model via Optimal Transport

1 code implementation ICLR 2021 He Zhao, Dinh Phung, Viet Huynh, Trung Le, Wray Buntine

Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis.

Topic Models

Quaternion Graph Neural Networks

1 code implementation12 Aug 2020 Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

As demonstrated, the Quaternion space, a hyper-complex vector space, provides highly meaningful computations and analogical calculus through Hamilton product compared to the Euclidean and complex vector spaces.

General Classification Graph Classification +3

MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models

no code implementations6 Aug 2020 Thanh Nguyen-Duc, He Zhao, Jianfei Cai, Dinh Phung

To interpret the teacher model and assist the learning of the student, an explainer module is introduced to highlight the regions of an input that are important for the predictions of the teacher model.

Image Classification Knowledge Distillation +1

Improving Adversarial Robustness by Enforcing Local and Global Compactness

1 code implementation ECCV 2020 Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham, Dinh Phung

The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application.

Adversarial Robustness Clustering

A Self-Attention Network based Node Embedding Model

1 code implementation22 Jun 2020 Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

Despite several signs of progress have been made recently, limited research has been conducted for an inductive setting where embeddings are required for newly unseen nodes -- a setting encountered commonly in practical applications of deep learning for graph networks.

General Classification Link Prediction +1

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

1 code implementation16 Apr 2020 Mahmoud Hossam, Trung Le, Viet Huynh, Michael Papasimeon, Dinh Phung

One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals.

reinforcement-learning Reinforcement Learning (RL)

Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes

no code implementations10 Oct 2019 Tam Le, Viet Huynh, Nhat Ho, Dinh Phung, Makoto Yamada

We study in this paper a variant of Wasserstein barycenter problem, which we refer to as tree-Wasserstein barycenter, by leveraging a specific class of ground metrics, namely tree metrics, for Wasserstein distance.

Clustering

Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions

no code implementations3 Oct 2019 He Zhao, Trung Le, Paul Montague, Olivier De Vel, Tamas Abraham, Dinh Phung

Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.

Adversarial Attack Translation

Universal Graph Transformer Self-Attention Networks

1 code implementation26 Sep 2019 Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

The transformer self-attention network has been extensively used in research domains such as computer vision, image processing, and natural language processing.

General Classification Graph Classification +1

Unsupervised Universal Self-Attention Network for Graph Classification

no code implementations25 Sep 2019 Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

Thus, U2GAN can address the weaknesses in the existing models in order to produce plausible node embeddings whose sum is the final embedding of the whole graph.

Graph Classification Graph Embedding

A Relational Memory-based Embedding Model for Triple Classification and Search Personalization

1 code implementation ACL 2020 Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung

Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems.

General Classification Knowledge Graph Embedding +2

Learning How to Active Learn by Dreaming

1 code implementation ACL 2019 Thuy-Trang Vu, Ming Liu, Dinh Phung, Gholamreza Haffari

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.

Active Learning named-entity-recognition +5

Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection

no code implementations ICLR 2019 Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel, Lizhen Qu

Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security.

Computer Security Vulnerability Detection

When Can Neural Networks Learn Connected Decision Regions?

no code implementations25 Jan 2019 Trung Le, Dinh Phung

Previous work has questioned the conditions under which the decision regions of a neural network are connected and further showed the implications of the corresponding theory to the problem of adversarial manipulation of classifiers.

On Deep Domain Adaptation: Some Theoretical Understandings

no code implementations15 Nov 2018 Trung Le, Khanh Nguyen, Nhat Ho, Hung Bui, Dinh Phung

The underlying idea of deep domain adaptation is to bridge the gap between source and target domains in a joint space so that a supervised classifier trained on labeled source data can be nicely transferred to the target domain.

Domain Adaptation Transfer Learning

Probabilistic Multilevel Clustering via Composite Transportation Distance

no code implementations29 Oct 2018 Nhat Ho, Viet Huynh, Dinh Phung, Michael. I. Jordan

We propose a novel probabilistic approach to multilevel clustering problems based on composite transportation distance, which is a variant of transportation distance where the underlying metric is Kullback-Leibler divergence.

Clustering

Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models

no code implementations3 May 2018 Hung Vu, Tu Dinh Nguyen, Dinh Phung

Abnormal event detection is one of the important objectives in research and practical applications of video surveillance.

Anomaly Detection Clustering +3

A Capsule Network-based Embedding Model for Search Personalization

no code implementations12 Apr 2018 Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Phung

After that, the 3-column matrix is fed into a deep learning architecture to re-rank the search results returned by a basis ranker.

MGAN: Training Generative Adversarial Nets with Multiple Generators

1 code implementation ICLR 2018 Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung

We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem.

KGAN: How to Break The Minimax Game in GAN

no code implementations6 Nov 2017 Trung Le, Tu Dinh Nguyen, Dinh Phung

In this paper, we propose a new viewpoint for GANs, which is termed as the minimizing general loss viewpoint.

General Classification

Analogical-based Bayesian Optimization

no code implementations19 Sep 2017 Trung Le, Khanh Nguyen, Tu Dinh Nguyen, Dinh Phung

With this spirit, in this paper, we propose Analogical-based Bayesian Optimization that can maximize black-box function over a domain where only a similarity score can be defined.

Bayesian Optimization Gaussian Processes

Dual Discriminator Generative Adversarial Nets

2 code implementations NeurIPS 2017 Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Phung

We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem.

Ranked #18 on Image Generation on STL-10 (Inception score metric)

Generative Adversarial Network

Energy-based Models for Video Anomaly Detection

no code implementations17 Aug 2017 Hung Vu, Dinh Phung, Tu Dinh Nguyen, Anthony Trevors, Svetha Venkatesh

Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection.

Anomaly Detection Feature Engineering +2

Geometric Enclosing Networks

no code implementations16 Aug 2017 Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Phung

Training model to generate data has increasingly attracted research attention and become important in modern world applications.

Multi-Generator Generative Adversarial Nets

no code implementations8 Aug 2017 Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung

A minimax formulation is able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN.

Multilevel Clustering via Wasserstein Means

1 code implementation ICML 2017 Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data.

Clustering

A Random Finite Set Model for Data Clustering

no code implementations14 Mar 2017 Dinh Phung, Ba-Ngu Bo

The goal of data clustering is to partition data points into groups to minimize a given objective function.

Clustering

Model-Based Multiple Instance Learning

no code implementations7 Mar 2017 Ba-Ngu Vo, Dinh Phung, Quang N. Tran, Ba-Tuong Vo

While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning.

Clustering Decision Making +3

Clustering For Point Pattern Data

no code implementations8 Feb 2017 Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo

However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources.

Clustering

Control Matching via Discharge Code Sequences

no code implementations2 Dec 2016 Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh

In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220, 000 patients.

Dual Space Gradient Descent for Online Learning

no code implementations NeurIPS 2016 Trung Le, Tu Nguyen, Vu Nguyen, Dinh Phung

However, this approach still suffers from a serious shortcoming as it needs to use a high dimensional random feature space to achieve a sufficiently accurate kernel approximation.

Stabilizing Linear Prediction Models using Autoencoder

no code implementations28 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.

Column Networks for Collective Classification

1 code implementation15 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.

Classification General Classification +2

Outlier Detection on Mixed-Type Data: An Energy-based Approach

1 code implementation17 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.

Outlier Detection Vocal Bursts Type Prediction

Faster Training of Very Deep Networks Via p-Norm Gates

no code implementations11 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.

Machine Translation Translation

Scalable Semi-supervised Learning with Graph-based Kernel Machine

no code implementations22 Jun 2016 Trung Le, Khanh Nguyen, Van Nguyen, Vu Nguyen, Dinh Phung

Acquiring labels are often costly, whereas unlabeled data are usually easy to obtain in modern machine learning applications.

BIG-bench Machine Learning

Approximation Vector Machines for Large-scale Online Learning

1 code implementation22 Apr 2016 Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Phung

One of the most challenging problems in kernel online learning is to bound the model size and to promote the model sparsity.

General Classification regression

Learning deep representation of multityped objects and tasks

no code implementations4 Mar 2016 Truyen Tran, Dinh Phung, Svetha Venkatesh

We introduce a deep multitask architecture to integrate multityped representations of multimodal objects.

Image Retrieval Retrieval

Choice by Elimination via Deep Neural Networks

no code implementations17 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.

Learning-To-Rank

Collaborative filtering via sparse Markov random fields

no code implementations9 Feb 2016 Truyen Tran, Dinh Phung, Svetha Venkatesh

Recommender systems play a central role in providing individualized access to information and services.

Collaborative Filtering Movie Recommendation +1

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

1 code implementation1 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.

Discovering topic structures of a temporally evolving document corpus

no code implementations25 Dec 2015 Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung, Svetha Venkatesh

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.

Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature

no code implementations8 Feb 2015 Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung, Svetha Venkatesh

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.

MCMC for Hierarchical Semi-Markov Conditional Random Fields

no code implementations6 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.

Thurstonian Boltzmann Machines: Learning from Multiple Inequalities

no code implementations1 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.

Collaborative Filtering Handwritten Digit Recognition

Learning Structured Outputs from Partial Labels using Forest Ensemble

no code implementations24 Jul 2014 Truyen Tran, Dinh Phung, Svetha Venkatesh

Learning structured outputs with general structures is computationally challenging, except for tree-structured models.

Stabilizing Sparse Cox Model using Clinical Structures in Electronic Medical Records

no code implementations23 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.

feature selection

Learning Rank Functionals: An Empirical Study

no code implementations23 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.

Information Retrieval Learning-To-Rank +3

Tree-based iterated local search for Markov random fields with applications in image analysis

no code implementations22 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.

Image Denoising Stereo Matching +1

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

no code implementations9 Jan 2014 Vu Nguyen, Dinh Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters.

Clustering

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