A bio-inspired incremental learning architecture for applied perceptual problems
We present a biologically inspired architecture for incremental learning that remains
resource-efficient even in the face of very high data dimensionalities (> 1000) that are …
resource-efficient even in the face of very high data dimensionalities (> 1000) that are …
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps
An intelligent system capable of continual learning is one that can process and extract
knowledge from potentially infinitely long streams of pattern vectors. The major challenge …
knowledge from potentially infinitely long streams of pattern vectors. The major challenge …
A generative learning approach to sensor fusion and change detection
We present a system for performing multi-sensor fusion that learns from experience, ie, from
training data and propose that learning methods are the most appropriate approaches to …
training data and propose that learning methods are the most appropriate approaches to …
Incremental learning with self-organizing maps
We present a novel use for self-organizing maps (SOMs) as an essential building block for
incremental learning algorithms. SOMs are very well suited for this purpose because they …
incremental learning algorithms. SOMs are very well suited for this purpose because they …
Reducing catastrophic forgetting in self organizing maps with internally-induced generative replay (student abstract)
A lifelong learning agent is able to continually learn from potentially infinite streams of
pattern sensory data. One major historic difficulty in building agents that adapt in this way is …
pattern sensory data. One major historic difficulty in building agents that adapt in this way is …
Incremental learning with a homeostatic self-organizing neural model
A Gepperth - Neural Computing and Applications, 2020 - Springer
We present a new self-organized neural model that we term resilient self-organizing tissue
(ReST), which can be run as a convolutional neural network, possesses ac^ ∞ c∞ energy …
(ReST), which can be run as a convolutional neural network, possesses ac^ ∞ c∞ energy …
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay
A lifelong learning agent is able to continually learn from potentially infinite streams of
pattern sensory data. One major historic difficulty in building agents that adapt in this way is …
pattern sensory data. One major historic difficulty in building agents that adapt in this way is …
[ΒΙΒΛΙΟ][B] Reducing Catastrophic Forgetting in Self-Organizing Maps
HUM Vaidya - 2021 - search.proquest.com
An agent that is capable of continual or lifelong learning is able to continuously learn from
potentially infinite streams of pattern sensory data. One major historic difficulty in building …
potentially infinite streams of pattern sensory data. One major historic difficulty in building …
New learning paradigms for real-world environment perception
A Gepperth - 2016 - hal.science
In this document, I first analyze some of the reasons why real-world environment perception
is still strongly inferior to human perception in overall accuracy and reliability. In particular, I …
is still strongly inferior to human perception in overall accuracy and reliability. In particular, I …