341 citations · 360 across the 10 of their papers we have counts for
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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…
SSSE: Efficiently Erasing Samples from Trained Machine Learning Models
Alexandra Peste, Dan Alistarh, Christoph H. Lampert
The availability of large amounts of user-provided data has been key to the success of machine learning for many real-world tasks. Recently, an increasing awareness has emerged tha…
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…
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun +2
The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Similarly to their biol…