11 citations · 14 across the 7 of their papers we have counts for
5 papers · 1 filter
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar +1
It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity co…
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization
Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova +3
Obtaining versions of deep neural networks that are both highly-accurate and highly-sparse is one of the main challenges in the area of model compression, and several high-performa…
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks
Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova +2
We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is gene…
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
Alexandra Peste, Eugenia Iofinova, Adrian Vladu +1
The increasing computational requirements of deep neural networks (DNNs) have led to significant interest in obtaining DNN models that are sparse, yet accurate. Recent work has inv…
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data
Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert
Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of…