◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Dinh Q. Phung

65 papers hereh-index 4410.1k citations314 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author39
  • last author24

Across the 64 of 65 papers where every author was matched, so the position is known.

fields
  • cs.LG27
  • stat.ML20
  • cs.IR6
  • cs.CL4
  • cs.CV3
  • cs.SI2
same name
  • Dinh Q. Phung — 90 papers, h 26
  • Dinh Q. Phung — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20102021
most citedA Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

892 citations · 2.3k across the 60 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020

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

Thanh Nguyen-Duc, He Zhao, Jianfei Cai +1

Deep learning methods usually require a large amount of training data and lack interpretability. In this paper, we propose a novel knowledge distillation and model interpretation f…

cs.LG2020

Improving Adversarial Robustness by Enforcing Local and Global Compactness

Anh Bui, Trung Le, He Zhao +4

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

cs.LG2020

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…

cs.LG2020

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

Mahmoud Hossam, Trung Le, Viet Huynh +2

One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals. Current sequential generative models mainly gener…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.