1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures
Anastasiia Prutianova, Alexey Zaytsev, Chung-Kuei Lee +2
Existing neural networks are memory-consuming and computationally intensive, making deploying them challenging in resource-constrained environments. However, there are various meth…
cs.CV2022★ 1 cited
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise
Egor Shvetsov, Dmitry Osin, Alexey Zaytsev +4
There is a constant need for high-performing and computationally efficient neural network models for image super-resolution: computationally efficient models can be used via low-ca…