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20142023
most citedSpeeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

330 citations · 358 across the 13 of their papers we have counts for

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13 papers · 1 filter

cs.RO2023

Memory-efficient particle filter recurrent neural network for object localization

Roman Korkin, Ivan Oseledets, Aleksandr Katrutsa

This study proposes a novel memory-efficient recurrent neural network (RNN) architecture specified to solve the object localization problem. This problem is to recover the object s…

math.NA2023

On the structure of the Schur complement matrix for the Stokes equation

Vladislav Pimanov, Ekaterina Muravleva, Ivan Oseledets +1

In this paper, we investigate the structure of the Schur complement matrix for the fully-staggered finite-difference discretization of the stationary Stokes equation. Specifically,…

cs.CV2023

Unsupervised evaluation of GAN sample quality: Introducing the TTJac Score

Egor Sevriugov, Ivan Oseledets

Evaluation metrics are essential for assessing the performance of generative models in image synthesis. However, existing metrics often involve high memory and time consumption as…

cs.CV2023

Robust GAN inversion

Egor Sevriugov, Ivan Oseledets

Recent advancements in real image editing have been attributed to the exploration of Generative Adversarial Networks (GANs) latent space. However, the main challenge of this proced…

cs.LG2023

Quantization Aware Factorization for Deep Neural Network Compression

Daria Cherniuk, Stanislav Abukhovich, Anh-Huy Phan +3

Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks. Due to memory and power consumption limitatio…

cs.AI20231 cited

Efficient GPT Model Pre-training using Tensor Train Matrix Representation

Viktoriia Chekalina, Georgii Novikov, Julia Gusak +2

Large-scale transformer models have shown remarkable performance in language modelling tasks. However, such models feature billions of parameters, leading to difficulties in their…