AML-P.1: Adversarial Machine Learning II |
Symposium: Signal Processing for Adversarial Machine Learning |
Session Type: Poster |
Time: Thursday, November 29, 15:50 - 17:20 |
Location: Sleeping Beauty Pavilion |
Session Chairs: Sijia Liu, IBM and Pin-Yu Chen, IBM Research AI |
Poster Board: 1 |
AML-P.1.1: MULTI-VIEW FRAME RECONSTRUCTION WITH CONDITIONAL GAN |
Tahmida Mahmud; University of California, Riverside |
Mohammad Billah; University of California, Riverside |
Amit Roy-Chowdhury; University of California, Riverside |
Poster Board: 2 |
AML-P.1.2: REINFORCED ADVERSARIAL ATTACKS ON DEEP NEURAL NETWORKS USING ADMM |
Pu Zhao; Northeastern University |
Kaidi Xu; Northeastern University |
Tianyun Zhang; Syracuse University |
Makan Fardad; Syracuse University |
Yanzhi Wang; Northeastern University |
Xue Lin; Northeastern University |
Poster Board: 3 |
AML-P.1.3: IS ORDERED WEIGHTED L1 REGULARIZED REGRESSION ROBUST TO ADVERSARIAL PERTURBATION? A CASE STUDY ON OSCAR |
Pin-Yu Chen; IBM Research AI |
Bhanukiran Vinzamuri; IBM Research AI |
Sijia Liu; IBM Research AI |
Poster Board: 4 |
AML-P.1.4: ZEROTH-ORDER STOCHASTIC PROJECTED GRADIENT DESCENT FOR NONCONVEX OPTIMIZATION |
Sijia Liu; IBM Research |
Xingguo Li; University of Minnesota |
Pin-Yu Chen; IBM Research |
Jarvis Haupt; University of Minnesota |
Lisa Amini; IBM Research |
Poster Board: 5 |
AML-P.1.5: ON THE TRADEOFF BETWEEN MODE COLLAPSE AND SAMPLE QUALITY IN GENERATIVE ADVERSARIAL NETWORKS |
Sudarshan Adiga; University of Arizona |
Mohamed Adel Attia; University of Arizona |
Wei-Ting Chang; University of Arizona |
Ravi Tandon; University of Arizona |
Poster Board: 6 |
AML-P.1.6: RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES |
Amish Goel; University of Illinois at Urbana–Champaign |
Pierre Moulin; University of Illinois at Urbana–Champaign |