Oct 18, 2021 · DendSOM consists of a single layer of SOMs, which extract patterns from specific regions of the input space accompanied by a set of hit matrices ...
Oct 18, 2021 · Here, we propose a novel algorithm inspired by biological neurons, termed Dendritic-Self-Organizing Map (DendSOM). DendSOM consists of a single ...
DendSOM performs unsupervised feature extraction as it does not use labels for targeted updating of the weights and outperforms classical SOMs and several ...
Oct 18, 2021 · DendSOM consists of a single layer of SOMs, which extract patterns from specific regions of the input space accompanied by a set of hit matrices ...
Dendritic Self-Organizing Maps for Continual Learning Pinitas K, Chavlis S, Poirazi P arXiv, Oct 2021 | URL: arXiv:2110.13611. Tagged 2021, Machine Learning ...
This work proposes a memoryless method that combines standard supervised neural networks with self-organizing maps to solve the continual learning problem, ...
We propose a memoryless method that combines standard supervised neural networks with self-organizing maps to solve the continual learning problem. The role of ...
People also ask
What is a self-organizing map in deep learning?
What is BMU in Self-Organizing Maps?
What Self-Organizing Maps can also be considered as the instance of?
What are the advantages of Self-Organizing Maps?
The role of the self-organizing map is to adaptively cluster the inputs into appropriate task contexts - without explicit labels - and allocate network ...
Dendritic Self-Organizing Maps for Continual Learning ... Current deep learning architectures show remarkable performance when trained in large-scale, controlled ...
Sep 10, 2019 · Self-organizing maps (SOMs) are an unsupervised form of Machine Learning that can be used to cluster data that has many features.