Can Bakiskan, Metehan Cekic, Upamanyu Madhow, "Early Layers Are More Important For Adversarial Robustness," in International Conference on Machine Learning (ICML) Workshop - New Frontiers in Adversarial Machine Learning, 2022

Metehan Cekic, Can Bakiskan, Upamanyu Madhow, "Layerwise Hebbian/anti-Hebbian (HaH) Learning In Deep Networks: A Neuro-inspired Approach To Robustness," in International Conference on Machine Learning (ICML) Workshop - New Frontiers in Adversarial Machine Learning, 2022

Can Bakiskan*, Metehan Cekic*, Upamanyu Madhow, "Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations," in IEEE International Conference on Image Processing (ICIP), 2022

Metehan Cekic, Can Bakiskan, Upamanyu Madhow, "Towards Robust, Interpretable Neural Networks via Hebbian/anti-Hebbian Learning: A Software Framework for Training with Feature-Based Costs," in Software Impacts Journal, 2022

Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer, Upamanyu Madhow, "Sparse Coding Frontend for Robust Neural Networks," in International Conference on Learning Representations (ICLR) Workshop on Security and Safety in Machine Learning Systems, 2021

Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer, Upamanyu Madhow, "A Neuro-Inspired Autoencoding Defense Against Adversarial Attacks," in IEEE International Conference on Image Processing (ICIP), 2021

Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic, Upamanyu Madhow, Ramtin Pedarsani, "Polarizing Front Ends for Robust CNNs," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020