Name
Abstract | ||
|---|---|---|
We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks. Instead of using backpropagation to learn features, GLNs have a distributed and local credit assignment mechanism based on optimizing a convex objective. This gives rise to many desirable properties including universality, data-efficient online learning, trivial interpretability and robustness to catastrophic forgetting. We extend the GLN framework from classification to multiple regression and density modelling by generalizing geometric mixing to a product of Gaussian densities. The G-GLN achieves competitive or state-of-the-art performance on several univariate and multivariate regression benchmarks, and we demonstrate its applicability to practical tasks including online contextual bandits and density estimation via denoising. |
Year | Venue | DocType |
|---|---|---|
2020 | NIPS 2020 | Conference |
Volume | Citations | PageRank |
33 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Budden | 1 | 167 | 18.45 |
| Adam Henry Marblestone | 2 | 37 | 4.29 |
| Sezener Eren | 3 | 0 | 1.69 |
| Tor Lattimore | 4 | 174 | 29.15 |
| Greg Wayne | 5 | 592 | 31.86 |
| Joel Veness | 6 | 3152 | 152.40 |