Deep function learning with reconciled polynomial network
Project description
Introduction
tinybig
is a Python library developed by the IFM Lab for deep function learning model designing and building.
- Official Website: https://www.tinybig.org/
- PyPI: https://pypi.org/project/tinybig/
Citation
-
The RPN Paper: https://github.com/jwzhanggy/tinyBIG/blob/main/docs/assets/files/rpn_paper.pdf
-
The RPN Paper at arXiv: TBD
If you find tinybig
and RPN useful in your work, please cite the RPN paper as follows:
@article{Zhang2024RPN,
title={RPN: Reconciled Polynomial Network Towards Unifying PGMs, Kernel SVMs, MLP and KAN},
author={Jiawei Zhang},
journal={ArXiv},
year={2024},
volume={},
}
Installation
Pip
pip install tinybig
Source
git clone https://github.com/jwzhanggy/tinyBIG.git
After entering the downloaded source code directory, tinybig can be installed with the following command:
python setup.py install
If you don't have setuptools
installed locally, please consider to first install setuptools
:
pip install setuptools
Dependency
Please download the requirements.txt file, and install all the dependency packages:
pip install -r requirements.txt
Library Organizations
Components | Descriptions |
---|---|
tinybig |
a deep function learning library like torch.nn, deeply integrated with autograd |
tinybig.expansion |
a library providing the "data expansion functions" for multi-modal data effective expansions |
tinybig.reconciliation |
a library providing the "parameter reconciliation functions" for parameter efficient learning |
tinybig.remainder |
a library providing the "remainder functions" for complementary information addition |
tinybig.module |
a library providing the basic building blocks for RPN model designing and implementation |
tinybig.model |
a library providing the RPN models for addressing various deep function learning tasks |
tinybig.config |
a library providing model component instantiation from textual configuration descriptions |
tinybig.learner |
a library providing the learners that can be used for RPN model training and testing |
tinybig.data |
a library providing multi-modal datasets for solving various deep function learning tasks |
tinybig.output |
a library providing the processing method interfaces for output processing, saving and loading |
tinybig.metric |
a library providing the metrics that can be used for RPN model performance evaluation |
tinybig.util |
a library of utility functions for RPN model design, implementation and learning |
Tutorials
Tutorial ID | Tutorial Title | Last Update |
---|---|---|
Tutorial 0 | Quickstart Tutorial | July 6, 20204 |
Tutorial 1 | Data Expansion Functions | July 7, 2024 |
Tutorial 2 | Extended and Nested Data Expansion | TBD |
License & Copyright
Copyright © 2024 IFM Lab. All rights reserved.
tinybig
source code is published under the terms of the MIT License.tinybig
's documentation and the RPN papers are licensed under a Creative Commons Attribution-Share Alike 4.0 Unported License (CC BY-SA 4.0).
Project details
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