11 citations · 14 across the 7 of their papers we have counts for
7 papers
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…
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures
Eugenia Iofinova, Alexandra Peste, Dan Alistarh
Pruning - that is, setting a significant subset of the parameters of a neural network to zero - is one of the most popular methods of model compression. Yet, several recent works h…
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…
How Well Do Sparse Imagenet Models Transfer?
Eugenia Iofinova, Alexandra Peste, Mark Kurtz +1
Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" specialized datasets. Generally, m…
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…