Shift: A zero flop, zero parameter alternative to spatial convolutions B Wu, A Wan, X Yue, P Jin, S Zhao, N Golmant, A Gholaminejad, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 410 | 2018 |
Artificial intelligence safety and security RV Yampolskiy CRC Press, 2018 | 115 | 2018 |
On the computational inefficiency of large batch sizes for stochastic gradient descent N Golmant, N Vemuri, Z Yao, V Feinberg, A Gholami, K Rothauge, ... arXiv preprint arXiv:1811.12941, 2018 | 79 | 2018 |
pytorch-hessian-eigenthings: Efficient PyTorch Hessian Eigendecomposition N Golmant, Z Yao, A Gholami, M Mahoney, J Gonzalez URL https://github.com/noahgolmant/pytorch-hessian-eigenthings, 2018 | 16* | 2018 |
Transferability of adversarial attacks in model-agnostic meta-learning R Edmunds, N Golmant, V Ramasesh, P Kuznetsov, P Patil, R Puri Deep Learning and Security Workshop (DLSW) in Singapore, 2017 | 13 | 2017 |
On the convergence of model-agnostic meta-learning N Golmant | 2 | 2019 |
Adversarial machine learning P Kuznetsov, R Edmunds, T Xiao, H Iqbal, R Puri, N Golmant, S Shih Artificial Intelligence Safety and Security, 235-248, 2018 | 2 | 2018 |
Kontradyktoryjne uczenie maszynowe P Kuznetsov, R Edmunds, T Xiao, H Iqbal, R Puri, N Golmant, S Shih Napędy i Sterowanie 23 (7/8), 80-89, 2021 | | 2021 |
An Empirical Exploration of Gradient Correlations in Deep Learning D Rothchild, R Fox, N Golmant, J Gonzalez, M Mahoney, K Rothauge, ... | | |
Derivations of the Univariate and Multivariate Normal Density A Francis, N Golmant | | |