Research paper / RESOURCE PROFILE

Learnable Companding Quantization for Accurate Low-bit Neural Networks

Publication: Learnable Companding Quantization for Accurate Low-bit Neural Networks. Published Mar 12, 2021.

Research & Papers
arxiv.orgPublished Mar 12, 2021
Visit source website ↗

Overview

Publication: Learnable Companding Quantization for Accurate Low-bit Neural Networks. Published Mar 12, 2021.

Publication information

Publication title
Learnable Companding Quantization for Accurate Low-bit Neural Networks
Authors
Kohei Yamamoto
Published
Mar 12, 2021
Last publication revision
Mar 12, 2021
Venue / journal
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 5029-5038
Subject areas
cs.CV, cs.LG
Publication source
arxiv.org · abs/2103.07156v1 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

https://arxiv.org/abs/2103.07156 ↗

Discovery references

References explain how this resource entered the collection. They do not represent an endorsement.

Explore related resources

Screenshot unavailableResource information and source links remain available. DeepIntoStreams/TSFM_Finance — reviewed source website screenshot
RepositoryChecked capture

DeepIntoStreams/TSFM_Finance

github.com

the official implementation of the paper “Re(visiting) Time Series Foundation Models in Finance”.

GitHub stars
31
Language
Jupyter Notebook
Code license
Apache-2.0
foundation-modeltime-seriesresearch-lab
Research & Papers Explore →
96 Authority 96 / 100 Cited by 2 resources in this directory 2 discovery sources Directory record updated: 2026-10-11 How scoring works →

Sign In

Don't have an account yet? Register

Forgot password?

Register

Reset Password

Please enter your username or email address, you will receive a link to create a new password via email.