Timezone: »
Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently shown that Bayesian dropout procedure not only improves gener- alization but also leads to extremely sparse neural architectures by automatically setting the individual noise magnitude per weight. However, this sparsity can hardly be used for acceleration since it is unstructured. In the paper, we propose a new Bayesian model that takes into account the computational structure of neural net- works and provides structured sparsity, e.g. removes neurons and/or convolutional channels in CNNs. To do this we inject noise to the neurons outputs while keeping the weights unregularized. We establish the probabilistic model with a proper truncated log-uniform prior over the noise and truncated log-normal variational approximation that ensures that the KL-term in the evidence lower bound is com- puted in closed-form. The model leads to structured sparsity by removing elements with a low SNR from the computation graph and provides significant acceleration on a number of deep neural architectures. The model is easy to implement as it can be formulated as a separate dropout-like layer.
Author Information
Kirill Neklyudov (Yandex)
Dmitry Molchanov (Yandex)
Arsenii Ashukha
Dmitry Vetrov (Higher School of Economics, Samsung AI Center, Moscow)
More from the Same Authors
-
2021 : Particle Dynamics for Learning EBMs »
Kirill Neklyudov · Priyank Jaini · Max Welling -
2021 : Particle Dynamics for Learning EBMs »
Kirill Neklyudov · Priyank Jaini · Max Welling -
2021 Poster: Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces »
Kirill Struminsky · Artyom Gadetsky · Denis Rakitin · Danil Karpushkin · Dmitry Vetrov -
2021 Poster: On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay »
Ekaterina Lobacheva · Maxim Kodryan · Nadezhda Chirkova · Andrey Malinin · Dmitry Vetrov -
2020 Poster: On Power Laws in Deep Ensembles »
Ekaterina Lobacheva · Nadezhda Chirkova · Maxim Kodryan · Dmitry Vetrov -
2020 Spotlight: On Power Laws in Deep Ensembles »
Ekaterina Lobacheva · Nadezhda Chirkova · Maxim Kodryan · Dmitry Vetrov -
2019 Poster: The Implicit Metropolis-Hastings Algorithm »
Kirill Neklyudov · Evgenii Egorov · Dmitry Vetrov -
2019 Poster: Importance Weighted Hierarchical Variational Inference »
Artem Sobolev · Dmitry Vetrov -
2019 Poster: A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models »
Maxim Kuznetsov · Daniil Polykovskiy · Dmitry Vetrov · Alex Zhebrak -
2019 Poster: A Simple Baseline for Bayesian Uncertainty in Deep Learning »
Wesley Maddox · Pavel Izmailov · Timur Garipov · Dmitry Vetrov · Andrew Gordon Wilson -
2018 : TBC 2 »
Dmitry Vetrov -
2018 Poster: Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs »
Timur Garipov · Pavel Izmailov · Dmitrii Podoprikhin · Dmitry Vetrov · Andrew Wilson -
2018 Spotlight: Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs »
Timur Garipov · Pavel Izmailov · Dmitrii Podoprikhin · Dmitry Vetrov · Andrew Wilson -
2016 Poster: PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions »
Mikhail Figurnov · Aizhan Ibraimova · Dmitry Vetrov · Pushmeet Kohli -
2015 Poster: M-Best-Diverse Labelings for Submodular Energies and Beyond »
Alexander Kirillov · Dmytro Shlezinger · Dmitry Vetrov · Carsten Rother · Bogdan Savchynskyy -
2015 Poster: Tensorizing Neural Networks »
Alexander Novikov · Dmitrii Podoprikhin · Anton Osokin · Dmitry Vetrov