Gated Linear Networks Article Swipe
Joel Veness
,
Tor Lattimore
,
David Budden
,
Avishkar Bhoopchand
,
Christopher Mattern
,
Agnieszka Grabska‐Barwińska
,
Eren Sezener
,
Jianan Wang
,
Péter Tóth
,
Simon Schmitt
,
Marcus Hütter
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v35i11.17202
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v35i11.17202
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distributed and local nature of their credit assignment mechanism; each neuron directly predicts the target, forgoing the ability to learn feature representations in favor of rapid online learning. Individual neurons are able to model nonlinear functions via the use of data-dependent gating in conjunction with online convex optimization. We show that this architecture gives rise to universal learning capabilities in the limit, with effective model capacity increasing as a function of network size in a manner comparable with deep ReLU networks. Furthermore, we demonstrate that the GLN learning mechanism possesses extraordinary resilience to catastrophic forgetting, performing almost on par to an MLP with dropout and Elastic Weight Consolidation on standard benchmarks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v35i11.17202
- https://ojs.aaai.org/index.php/AAAI/article/download/17202/17009
- OA Status
- diamond
- Cited By
- 3
- References
- 49
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2961192671
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2961192671Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/aaai.v35i11.17202Digital Object Identifier
- Title
-
Gated Linear NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-18Full publication date if available
- Authors
-
Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska‐Barwińska, Eren Sezener, Jianan Wang, Péter Tóth, Simon Schmitt, Marcus HütterList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v35i11.17202Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/17202/17009Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/17202/17009Direct OA link when available
- Concepts
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Computer science, Forgetting, Dropout (neural networks), Artificial intelligence, Deep learning, Artificial neural network, Activation function, Backpropagation, Feature (linguistics), Mechanism (biology), Machine learning, Epistemology, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
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-
2022: 1, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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49Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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