29 citations · 31 across the 3 of their papers we have counts for
4 papers
LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU
Evgeny Ponomarev, Sergey Matveev, Ivan Oseledets
A lot of deep learning applications are desired to be run on mobile devices. Both accuracy and inference time are meaningful for a lot of them. While the number of FLOPs is usually…
Using Reinforcement Learning in the Algorithmic Trading Problem
Evgeny Ponomarev, Ivan Oseledets, Andrzej Cichocki
The development of reinforced learning methods has extended application to many areas including algorithmic trading. In this paper trading on the stock exchange is interpreted into…
Reduced-Order Modeling of Deep Neural Networks
Julia Gusak, Talgat Daulbaev, Evgeny Ponomarev +2
We introduce a new method for speeding up the inference of deep neural networks. It is somewhat inspired by the reduced-order modeling techniques for dynamical systems.The cornerst…
MUSCO: Multi-Stage Compression of neural networks
Julia Gusak, Maksym Kholiavchenko, Evgeny Ponomarev +3
The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank…