3 citations · 3 across the 5 of their papers we have counts for
5 papers
Robust Multimodal Sentiment Analysis with Incomplete Modalities via Semantic-aware Completeness based Reconstruction
Han-Jun Choi, Byunggill Joe, Saim Shin +1
Recent multimodal sentiment analysis studies increasingly adopt text-centric fusion approaches to exploit the rich sentiment information inherent in the textual modality. However,…
Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future
Byunggill Joe, Insik Shin, Jihun Hamm
Recurrent models are frequently being used in online tasks such as autonomous driving, and a comprehensive study of their vulnerability is called for. Existing research is limited…
Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks
Byunggill Joe, Akshay Mehra, Insik Shin +1
Electronic Health Records (EHRs) provide a wealth of information for machine learning algorithms to predict the patient outcome from the data including diagnostic information, vita…
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection
Byunggill Joe, Jihun Hamm, Sung Ju Hwang +2
Although deep neural networks have shown promising performances on various tasks, they are susceptible to incorrect predictions induced by imperceptibly small perturbations in inpu…
Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection
Byunggill Joe, Sung Ju Hwang, Insik Shin
Although deep neural networks have shown promising performances on various tasks, even achieving human-level performance on some, they are shown to be susceptible to incorrect pred…