Search Results for author: Tu Dinh Nguyen

Found 21 papers, 11 papers with code

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

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

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

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

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.

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

Cannot find the paper you are looking for? You can Submit a new open access paper.