tinybig 0.1.1

tinybig library for deep function learning

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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 at arXiv: https://arxiv.org/abs/2407.04819

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},
    year={2024},
    eprint={2407.04819},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Installation

You can install tinybig either via pip or directly from the github source code.

Install via Pip

pip install tinybig

Install from 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

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

Examples

Example ID	Example Title	Released Date
Example 1	Elementary Function Approximation	July 7, 2024
Example 2	Composite Function Approximation	July 8, 2024
Example 3	Feynman Function Approximation	July 8, 2024
Example 4	MNIST Classification with Identity Reconciliation	July 8, 2024
Example 5	MNIST Classification with Dual LPHM Reconciliation	July 8, 2024
Example 6	CIFAR10 Object Detection in Images	July 8, 2024

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

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).