Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples

7 October 2020

Papers citing "Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples"

9 / 9 papers shown

Title
Trustworthy Actionable Perturbations Jesse Friedbaum Sudarshan Adiga Ravi Tandon AAML 38 2 0 18 May 2024
Adversarial Robustness for Tabular Data through Cost and Utility Awareness Klim Kireev B. Kulynych Carmela Troncoso AAML 26 16 0 27 Aug 2022
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks Salijona Dyrmishi Salah Ghamizi Thibault Simonetto Yves Le Traon Maxime Cordy AAML 32 16 0 07 Feb 2022
Deep Neural Networks and Tabular Data: A Survey V. Borisov Tobias Leemann Kathrin Seßler Johannes Haug Martin Pawelczyk Gjergji Kasneci LMTD 47 650 0 05 Oct 2021
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack Gilad Gressel Niranjan Hegde A. Sreekumar Rishikumar Radhakrishnan Kalyani Harikumar Michael C. Darling Krishnashree Achuthan AAML 67 16 0 28 Jun 2021
Adversarial Robustness with Non-uniform Perturbations Ece Naz Erdemir Jeffrey Bickford Luca Melis Sergul Aydore AAML 24 26 0 24 Feb 2021
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data F. Cartella Orlando Anunciação Yuki Funabiki D. Yamaguchi Toru Akishita Olivier Elshocht AAML 61 71 0 20 Jan 2021
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data Sergei Popov S. Morozov Artem Babenko LMTD 91 296 0 13 Sep 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 308 5,847 0 08 Jul 2016