US 2025/0094810 A1 · ADAPTABLE AND CONTINUALLY LEARNING NEURAL NETWORK ARCHITECTURE

Abstract

Method and apparatus for processing input information using an adaptable and continually learning neural network architecture comprising an encoder, at least one adaptor and at least one reconfigurator. The encoder, at least one reconfigurator and at least one adaptor determine whether the input information is out-of-distribution or in-distribution. If the input information is in distribution, the architecture extracts features from the input information, creates hyperdimensional vectors representing the features and classifies the hyperdimensional vectors. If the input information is out of distribution, the architecture creates at least one adaptor to operate with the encoder and the at least one reconfigurator to extract features from the input information, create hyperdimensional vectors representing the features and classify the hyperdimensional vectors.

Claims20

Dependent claims

An apparatus configured to process data using machine learning comprising:

an encoder configured to encode input data to detect features in the input data;
at least one reconfigurator, coupled to the encoder and comprising an at least one out-of-distribution detector configured to detect when the input data is out-of-distribution, configured to create at least one adaptor when the input data is out-of-distribution;
and
the at least one adaptor, coupled to the encoder and the at least one reconfigurator, configured to operate with the encoder and at least one reconfigurator to encode the out-of-distribution input data into an HD vector and classify the HD vector.

1 more element

Dependencies are inferred from the claim text.

Adaptable and continually learning neural network architecture

Life of this patent

Adaptable and continually learning neural network architecture

Life of this patent

Abstract

Claims20