BioGD: Bio-inspired robust gradient descent.

ABSTRACT: Recent research in machine learning pointed to the core problem of state-of-the-art models which impedes their widespread adoption in different domains. The models' inability to differentiate between noise and subtle, yet significant variation in data leads to their vulnerability to adversarial perturbations that cause wrong predictions with high confidence. The study is aimed at identifying whether the algorithms inspired by biological evolution may achieve better results in cases where brittle robustness properties are highly sensitive to the slight noise. To answer this question, we introduce the new robust gradient descent inspired by the stability and adaptability of biological systems to unknown and changing environments. The proposed optimization technique involves an open-ended ada

SUBMITTER: Kulikovskikh I 

PROVIDER: S-EPMC6611597 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature