604 citations · 716 across the 3 of their papers we have counts for
3 papers
Bayesian Incremental Learning for Deep Neural Networks
Max Kochurov, Timur Garipov, Dmitry Podoprikhin +3
In industrial machine learning pipelines, data often arrive in parts. Particularly in the case of deep neural networks, it may be too expensive to train the model from scratch each…
Ultimate tensorization: compressing convolutional and FC layers alike
Timur Garipov, Dmitry Podoprikhin, Alexander Novikov +1
Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity. To tackle this problem, [1] developed a tens…
Tensorizing Neural Networks
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin +1
Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational reso…