892 citations · 1.1k across the 17 of their papers we have counts for
12 papers · 1 filter
Quaternion Graph Neural Networks
Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
Recently, graph neural networks (GNNs) have become an important and active research direction in deep learning. It is worth noting that most of the existing GNN-based methods learn…
A Self-Attention Network based Node Embedding Model
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 se…
A Capsule Network-based Model for Learning Node Embeddings
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen +1
In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leverag…
Universal Graph Transformer Self-Attention Networks
Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
We introduce a transformer-based GNN model, named UGformer, to learn graph representations. In particular, we present two UGformer variants, wherein the first variant (publicized i…
KGAN: How to Break The Minimax Game in GAN
Trung Le, Tu Dinh Nguyen, Dinh Phung
Generative Adversarial Networks (GANs) were intuitively and attractively explained under the perspective of game theory, wherein two involving parties are a discriminator and a gen…
Analogical-based Bayesian Optimization
Trung Le, Khanh Nguyen, Tu Dinh Nguyen +1
Some real-world problems revolve to solve the optimization problem \max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box function and X might be the set of non…
Dual Discriminator Generative Adversarial Nets
Tu Dinh Nguyen, Trung Le, Hung Vu +1
We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN). Our idea is intuitive but proven to be very ef…
Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization
Tu Dinh Nguyen, Truyen Tran, Dinh Phung +1
The success of any machine learning system depends critically on effective representations of data. In many cases, it is desirable that a representation scheme uncovers the parts-b…
Statistical Latent Space Approach for Mixed Data Modelling and Applications
Tu Dinh Nguyen, Truyen Tran, Dinh Phung +1
The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodo…
Geometric Enclosing Networks
Trung Le, Hung Vu, Tu Dinh Nguyen +1
Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optim…
Multi-Generator Generative Adversarial Nets
Quan Hoang, Tu Dinh Nguyen, Trung Le +1
We propose a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ mult…
Approximation Vector Machines for Large-scale Online Learning
Trung Le, Tu Dinh Nguyen, Vu Nguyen +1
One of the most challenging problems in kernel online learning is to bound the model size and to promote the model sparsity. Sparse models not only improve computation and memory u…