892 citations · 2.3k across the 60 of their papers we have counts for
6 papers · 1 filter
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…
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
He Zhao, Trung Le, Paul Montague +3
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Recently, v…
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes
Tam Le, Viet Huynh, Nhat Ho +2
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…
On Efficient Multilevel Clustering via Wasserstein Distances
Viet Huynh, Nhat Ho, Nhan Dam +4
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 pote…
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
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. To t…
When Can Neural Networks Learn Connected Decision Regions?
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 th…