Identify Susceptible Locations in Medical Records via Adversarial
Attacks on Deep Predictive Models

Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models

13 February 2018

Fei Wang

Papers citing "Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models"

9 / 9 papers shown

Title
Surfacing Biases in Large Language Models using Contrastive Input Decoding G. Yona Or Honovich Itay Laish Roee Aharoni 27 11 0 12 May 2023
Rethinking Textual Adversarial Defense for Pre-trained Language Models Jiayi Wang Rongzhou Bao Zhuosheng Zhang Hai Zhao AAML SILM 17 11 0 21 Jul 2022
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare Muchao Ye Junyu Luo Guanjie Zheng Cao Xiao Ting Wang Fenglong Ma AAML 24 3 0 11 Dec 2021
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 31 55 0 05 Dec 2021
Evaluating the Robustness of Neural Language Models to Input Perturbations M. Moradi Matthias Samwald AAML 48 95 0 27 Aug 2021
Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks Byunggill Joe Akshay Mehra I. Shin Jihun Hamm 9 9 0 15 Jun 2021
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey W. Zhang Quan Z. Sheng A. Alhazmi Chenliang Li AAML 24 57 0 21 Jan 2019
Query-Efficient Black-Box Attack by Active Learning Pengcheng Li Jinfeng Yi Lijun Zhang AAML MLAU 21 54 0 13 Sep 2018
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models D. Su Huan Zhang Hongge Chen Jinfeng Yi Pin-Yu Chen Yupeng Gao VLM 18 387 0 05 Aug 2018