Anastasiya Mishchuk

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We introduce a loss for metric learning, which is inspired by the Lowe’s matching criterion for SIFT. We show that the proposed loss, that maximizes the distance between the closest positive and closest negative example in the batch, is better than complex regularization methods; it works well for both shallow and deep convolution network architectures.(More)
An analysis of long-term results of treatment of 115 patients has shown that normalization of metabolic processes by anabolic hormones, vitamins, protein hydrolysates, aminoacid mixtures, fat emulsions, biostimulators (solcoseryl, methyluracil etc.) elevates nonspecific resistance of the organism, shortens the period of temporary labour invalidity,(More)
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