Timezone: »
Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by adopting a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network. We introduce two novelties in this paper: 1) we use hierarchical priors to prune nodes instead of individual weights, and 2) we use the posterior uncertainties to determine the optimal fixed point precision to encode the weights. Both factors significantly contribute to achieving the state of the art in terms of compression rates, while still staying competitive with methods designed to optimize for speed or energy efficiency.
Author Information
Christos Louizos (University of Amsterdam)
Karen Ullrich (University of Amsterdam)
Research scientist (s/h) at FAIR NY + collab. w/ Vector Institute. ❤️ Deep Learning + Information Theory. Previously, Machine Learning PhD at UoAmsterdam.
Max Welling (University of Amsterdam and University of California Irvine and CIFAR)
More from the Same Authors
-
2021 Spotlight: Lossy Compression for Lossless Prediction »
Yann Dubois · Benjamin Bloem-Reddy · Karen Ullrich · Chris Maddison -
2021 : Your Dataset is a Multiset and You Should Compress it Like One »
Daniel Severo · James Townsend · Ashish Khisti · Alireza Makhzani · Karen Ullrich -
2021 : Particle Dynamics for Learning EBMs »
Kirill Neklyudov · Priyank Jaini · Max Welling -
2022 : Program Synthesis for Integer Sequence Generation »
Natasha Butt · Auke Wiggers · Taco Cohen · Max Welling -
2022 : Invited Talk #4, The Fifth Paradigm of Scientific Discovery, Max Welling »
Max Welling -
2021 : Your Dataset is a Multiset and You Should Compress it Like One »
Daniel Severo · James Townsend · Ashish Khisti · Alireza Makhzani · Karen Ullrich -
2021 : Particle Dynamics for Learning EBMs »
Kirill Neklyudov · Priyank Jaini · Max Welling -
2021 : General Discussion 1 - What is out of distribution (OOD) generalization and why is it important? with Yoshua Bengio, Leyla Isik, Max Welling »
Yoshua Bengio · Leyla Isik · Max Welling · Joshua T Vogelstein · Weiwei Yang -
2021 : Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders »
T. Anderson Keller · Qinghe Gao · Max Welling -
2021 : Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders »
T. Anderson Keller · Qinghe Gao · Max Welling -
2021 Workshop: AI for Science: Mind the Gaps »
Payal Chandak · Yuanqi Du · Tianfan Fu · Wenhao Gao · Kexin Huang · Shengchao Liu · Ziming Liu · Gabriel Spadon · Max Tegmark · Hanchen Wang · Adrian Weller · Max Welling · Marinka Zitnik -
2021 Poster: Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions »
Emiel Hoogeboom · Didrik Nielsen · Priyank Jaini · Patrick Forré · Max Welling -
2021 Poster: Topographic VAEs learn Equivariant Capsules »
T. Anderson Keller · Max Welling -
2021 Poster: Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent »
Priyank Jaini · Lars Holdijk · Max Welling -
2021 Poster: E(n) Equivariant Normalizing Flows »
Victor Garcia Satorras · Emiel Hoogeboom · Fabian Fuchs · Ingmar Posner · Max Welling -
2021 Poster: Modality-Agnostic Topology Aware Localization »
Farhad Ghazvinian Zanjani · Ilia Karmanov · Hanno Ackermann · Daniel Dijkman · Simone Merlin · Max Welling · Fatih Porikli -
2021 Poster: Lossy Compression for Lossless Prediction »
Yann Dubois · Benjamin Bloem-Reddy · Karen Ullrich · Chris Maddison -
2021 Oral: E(n) Equivariant Normalizing Flows »
Victor Garcia Satorras · Emiel Hoogeboom · Fabian Fuchs · Ingmar Posner · Max Welling -
2019 : Keynote - ML »
Max Welling -
2019 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
Christos Louizos · Uri Shalit · Joris Mooij · David Sontag · Richard Zemel · Max Welling -
2016 Workshop: Bayesian Deep Learning »
Yarin Gal · Christos Louizos · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2015 Poster: Bayesian dark knowledge »
Anoop Korattikara Balan · Vivek Rathod · Kevin Murphy · Max Welling