A Gated Linear Network, or GLN, is a type of backpropagationfree neural architecture. 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 can model nonlinear functions via the use of datadependent gating in conjunction with online convex optimization.
GLNs are feedforward networks composed of many layers of gated geometric mixing neurons as shown in the Figure . Each neuron in a given layer outputs a gated geometric mixture of the predictions from the previous layer, with the final layer consisting of just a single neuron. In a supervised learning setting, a $\mathrm{GLN}$ is trained on (side information, base predictions, label) triplets $\left(z_{t}, p_{t}, x_{t}\right)_{t=1,2,3, \ldots}$ derived from inputlabel pairs $\left(z_{t}, x_{t}\right)$. There are two types of input to neurons in the network: the first is the side information $z_{t}$, which can be thought of as the input features; the second is the input to the neuron, which will be the predictions output by the previous layer, or in the case of layer 0 , some (optionally) provided base predictions $p_{t}$ that typically will be a function of $z_{t} .$ Each neuron will also take in a constant bias prediction, which helps empirically and is essential for universality guarantees.
Weights are learnt in a Gated Linear Network using Online Gradient Descent (OGD) locally at each neuron. They key observation is that as each neuron $(i, k)$ in layers $i>0$ is itself a gated geometric mixture, all of these neurons can be thought of as individually predicting the target. Given side information $z$ , each neuron $(i, k)$ suffers a loss convex in its active weights $u:=w_{i k c_{i k}(z)}$ of $$ \ell_{t}(u):=\log \left(\operatorname{GEO}_{u}\left(x_{t} ; p_{i1}\right)\right) $$
Source: Gated Linear NetworksPaper  Code  Results  Date  Stars 

Task  Papers  Share 

Graph Learning  2  18.18% 
MultiArmed Bandits  2  18.18% 
Indoor Localization  1  9.09% 
Visual Place Recognition  1  9.09% 
Gait Recognition  1  9.09% 
Combinatorial Optimization  1  9.09% 
Denoising  1  9.09% 
Density Estimation  1  9.09% 
Image Classification  1  9.09% 
Component  Type 


🤖 No Components Found  You can add them if they exist; e.g. Mask RCNN uses RoIAlign 