Training Universal Adversarial Perturbations with Alternating Loss Functions

Şen, Deniz
Karlı, Berat Tuna
Temizel, Alptekin
36th AAAI Conference on Artificial Intelligence, Adversarial Machine Learning and Beyond Workshop


Training Recurrent Neural Networks Using Tabu Search Algorithm
Karaboğa, Derviş; Kalınlı, Adem (1996-06-04)
There are several modern heuristic optimisation techniques, such as neural networks, genetic algorithms, simulated annealing and tabu search algorithms. Of these algorithms, the tabu search is quite a new, promising search technique for numeric problems, especially for nonlinear problems. However, the convergence speed of the standard tabu search to the global optimum is initial-solution-dependent, since it is a form of iterative search. In this paper, a new model of tabu searching, which has been proposed ...
Training Elman Networks for Nonlinear System Identification Using Simulated Annealing Algorithm
Kalınlı, Adem (2003-07-04)
Training inverse BRDF with incomplete data for 3D reconstruction through photometric stereo /
Kileci, Samet; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2014)
In this thesis, missing data phenomena seen in a photometric stereo model is dealt with machine learning approaches. Photometric stereo model takes input images acquired with different illuminating conditions and predicts surface properties of an object. Specular regions appear on the images due to reflection for certain angle of light and camera and shadow regions appear because of surface structure of the object and light angle. Since specular and shadow regions degrade the performance of the photometric ...
Training ANFIS using genetic algorithm for dynamic systems identification
Haznedar, Bülent; Kalınlı, Adem (null; 2016-09-03)
Training Methodology for a Multiplication Free Implementable Operator Based Neural Networks
Yıldız, Ozan; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
Technological advances opened new possibilities for computing environments including smart phones, smart appliances, and drones. Engineers try to make these devices smart, self-sustaining through usage of machine learning techniques. However, most of the mobile environments have limited resources like memory, computing power and battery, and consequently traditional machine learning algorithms which require relatively high resources might not be suitable for them. Therefore, efficient versions of traditiona...
Citation Formats
D. Şen, B. T. Karlı, and A. Temizel, “Training Universal Adversarial Perturbations with Alternating Loss Functions,” presented at the 36th AAAI Conference on Artificial Intelligence, Adversarial Machine Learning and Beyond Workshop, Vancouver, Kanada, 2022, Accessed: 00, 2022. [Online]. Available: