Non-intrusive load monitoring (NILM), as an important part of intelligent electricity
consumption, improves the cognitive level of the load by analyzing the bus power in a …

Lipschitz constrained networks have gathered considerable attention in the deep learning
community, with usages ranging from Wasserstein distance estimation to the training of …

We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to
perform One Class Classification (OCC) by provably learning the Signed Distance Function …

Deep learning has demonstrated state-of-the-art performance for a variety of challenging
computer vision tasks. On one hand, this has enabled deep visual models to pave the way …

This work is devoted to methods for improving the quality of artificial neural networks,
developed by a group of scientists led by VL Arlazarov. It describes various approaches both …

We propose a new Novelty Detection and One Class classifier, based on the smoothness
properties of orthogonal neural network, and on the properties of Hinge Kantorovich …

This paper proposes a novel Orthogonal Classification Layer (OCL) utilizing Kasami
sequences for neural networks trained for classification problems. OCL consists of a fully …

This dissertation makes four major contributions towards the understanding of deep visual
models. Firstly, it develops a model-centric technique that peeks inside the internal …

Анотація Роботу присвячено вибору архітектури згорткової нейронної мережі для
розв'язання задач класифікації зображень. Побудовано математичну модель …