Blaize, Inc.

Adam P Geringer

Ilya A Balabin

Val Gene Cook

Dmitry Zakharchenko

Methods, systems, and apparatuses for unsupervised data drift detection for classification neural networks are disclosed. One method includes providing the data stream of images to a neural network, generating, by the neural network, class wise probabilities, storing each image of the data stream of images, storing the class wise probabilities generated by the neural network, comparing artifacts of images of the data stream at a first time with artifacts of images of the data stream at a second time, comparing artifacts produced by the class wise probabilities of the data stream retrieved from the stored class wise probabilities at a third time with artifacts produced by the class wise probabilities of the data stream retrieved from the stored class wise probabilities at a fourth time, and generating an informative communication based on the comparisons.

Unsupervised data drift detection for classification neural networks

Methods, systems and apparatuses for discovering novel artificial neural network architectures (ANN) architecture are disclosed. One method includes calculating ANN architecture fingerprints including an ANN architecture fingerprint of each of a plurality of existing ANN architectures, creating a plurality of next-generation candidate ANN architectures, calculating a plurality of next-generation candidate ANN architecture fingerprints including an ANN architecture fingerprint of each of the plurality of next-generation candidate ANN architectures, calculating ANN architecture pairwise similarities between each of the plurality of existing ANN architectures and each of the plurality of next-generation candidate ANN architectures using the plurality of existing ANN architecture fingerprints and the plurality of next-generation candidate ANN architecture fingerprints, retraining each of the plurality of next-generation candidate ANN architectures on the training dataset, obtaining a performance score of each of the next-generation candidate ANN architectures, and calculating a fitness score for each of the next-generation candidate ANN architectures.

Discovering novel artificial neural network architectures

Methods, systems and apparatuses for a custom artificial neural network (ANN) architecture are disclosed. One method includes selecting existing ANN architectures, calculating ANN architecture fingerprints, calculating ANN architecture pairwise similarities among the existing ANN architectures, calculating centrality scores for the existing ANN architectures using the ANN architecture pairwise similarities, calculating dataset pairwise similarities between the target dataset and each of the existing datasets using dataset fingerprints, calculating target performance scores for the existing ANN architectures on the target dataset using performance scores of the existing ANN architectures on the existing datasets and the dataset pairwise similarities, calculating interpolation weights for the existing ANN architectures using the target performance scores of the existing ANN architectures on the target dataset and the centrality scores, and obtaining the custom ANN architecture by interpolating among the existing ANN architectures using the calculated interpolation weights.

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Raleigh, NC, United States of America

Adam P Geringer