341 citations · 346 across the 2 of their papers we have counts for
3 papers
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…
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…
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
Septimia Sârbu, Riccardo Volpi, Alexandra Peşte +1
In this paper we propose two novel bounds for the log-likelihood based on Kullback-Leibler and the Rényi divergences, which can be used for variational inference and in particular…