132 citations · 705 across the 31 of their papers we have counts for
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Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Tolga Ergen, Arda Sahiner, Batu Ozturkler +3
Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks. Despite its empirical success, a full theoretical understanding…
Convex Regularization Behind Neural Reconstruction
Arda Sahiner, Morteza Mardani, Batu Ozturkler +2
Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders…
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms
Arda Sahiner, Tolga Ergen, John Pauly +1
We describe the convex semi-infinite dual of the two-layer vector-output ReLU neural network training problem. This semi-infinite dual admits a finite dimensional representation, b…
Degrees of Freedom Analysis of Unrolled Neural Networks
Morteza Mardani, Qingyun Sun, Vardan Papyan +3
Unrolled neural networks emerged recently as an effective model for learning inverse maps appearing in image restoration tasks. However, their generalization risk (i.e., test mean-…