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Probabilistic Adaptive Learning Mapper (PALM™) is the result of a research that gives answers to some issues raised in the DARPA L2 (Lifelong Learning) and LwLL (Learning with Less Labelling) programs. |
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Lifelong Learning (L2) Systems are
neural models capable of learning new data (in a supervised, unsupervised way
or through reward / punishment mechanisms) during their normal inferential
functioning. It is essential to clarify that the
learning algorithm of Error Back-Propagation on which Deep-Learning is based
cannot be considered in this category as it requires multiple learning cycles
(epochs) on all previously learned data. Learning iterations with just new
data would lead to Catastrophic Forgetting situations. Learning algorithms such as ART (Adaptive
Resonance Theory - Stephen Grossberg & Gail
Carpenter) and RCE (Restricted Coulomb Energy – Leon Cooper) on RBF (Radial
Basis Function) architecture allow the continuous learning of new data
avoiding catastrophic forgetting phenomena. Learning algorithms such as ART
(Adaptive Resonance Theory - Stephen Grossberg
& Gail Carpenter) and RCE (Restricted Coulomb Energy - Leon Cooper) on
RBF (Radial Basis Function) architecture allow the continuous learning of new
data avoiding catastrophic forgetting phenomena. Of course, there are always problems
inherent in any learning algorithm. Overfitting on
noisy data and lack of statistical consistency are two typical examples. Both ART and RCE can be modified
taking into account the data statistics (as in Probabilistic Neural Network -
PNN). This and other more sophisticated mechanisms can lead to the
achievement of the following results: ·
Supervised
learning when needed ·
Continuous
unsupervised learning that inherits from supervised learning ·
Continuous
adaptivity ·
Statistical
consistency ·
STM
(Short Term Memory) ·
LTM
(Long Term Memory) ·
Smart
statistical management of forgetting ·
Smart
statistical management of limited resources ·
Statistically
controlled balance of plasticity versus stability We have obtained these results with
the PALM™ (Probabilistic Adaptive Learning Mapper™)
neural model. Continuous learning can be achieved
also with our ROCKET™ (Registers Optimized Chip Knowledge Emulation
Technology™) which is an ultra high speed software simulator of a RBF-RCE neural
chip. Continuous learning can be achieved
also with our LASER™ (L-Anti-Spoofing Engineered Recognizer™) algorithm. Continuous learning with
RULES-EXTRACTION can also be achieved with our SHARP™ (Systolic Hebb Agnostic Resonance Perceptron™)
algorithm.
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