tinybig
is a Python library developed by the IFM Lab for deep function learning model designing and building.
- List of RPN Papers:
- RPN 1 (July 2024): https://arxiv.org/abs/2407.04819
- RPN 2 (November 2024): https://arxiv.org/abs/2411.11162
- RPN 3 (To be released ...)
tinybig
based Applications:- TBD
- Official Website: https://www.tinybig.org/
- PyPI: https://pypi.org/project/tinybig/
- IFM Lab: https://www.ifmlab.org/index.html
- Project Description in Chinese:
If you find tinybig
library and RPN papers useful in your work, please cite the RPN papers as follows:
@article{Zhang2024RPN_version1,
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}
}
@article{Zhang2024RPN_version2,
title={RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer},
author={Jiawei Zhang},
year={2024},
eprint={2411.11162},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
You can install tinybig
either via pip
or directly from the github source code.
pip install tinybig
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
Please download the requirements.txt file, and install all the dependency packages:
pip install -r requirements.txt
If you have successfully installed both tinybig
and the dependency packages, now you can use tinybig
in your projects.
To ensure that tinybig
was installed correctly, we can verify the installation by running the sample python code as follows:
>>> import torch
>>> import tinybig as tb
>>> expansion_func = tb.expansion.taylor_expansion()
>>> expansion_func(torch.Tensor([[1, 2]]))
The output should be something like:
tensor([[1., 2., 1., 2., 2., 4.]])
Tutorial ID | Tutorial Title | Last Update |
---|---|---|
Tutorial 0 | Quickstart Tutorial | July 6, 2024 |
Tutorial 1 | Data Expansion Functions | July 7, 2024 |
Tutorial 2 | Parameter Reconciliation Functions | November 28, 2024 |
Tutorial 3 | Data Interdependence Functions | December 1, 2024 |
Tutorial 4 | Structural Interdependence Functions | December 10, 2024 |
Example ID | Example Title | Released Date |
---|---|---|
Example 0 | Failure of KAN on Sparse Data | July 9, 2024 |
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 Image Object Recognition | July 8, 2024 |
Example 7 | IMDB Review Classification | July 9, 2024 |
Example 8 | AGNews Topic Classification | July 9, 2024 |
Example 9 | SST-2 Sentiment Classification | July 9, 2024 |
Example 10 | Iris Species Inference (Naive Probabilistic) | July 9, 2024 |
Example 11 | Diabetes Diagnosis (Comb. Probabilistic) | July 9, 2024 |
Example 12 | Banknote Authentication (Comb. Probabilistic) | July 9, 2024 |
Components | Descriptions |
---|---|
tinybig |
a deep function learning library like torch.nn, deeply integrated with autograd |
tinybig.model |
a library providing the RPN models for addressing various deep function learning tasks |
tinybig.module |
a library providing the basic building blocks for RPN model designing and implementation |
tinybig.layer |
a library providing the implemented layers for RPN model designing and implementation |
tinybig.head |
a library providing the implemented heads for RPN model designing and implementation |
tinybig.config |
a library providing model component instantiation from textual configuration descriptions |
tinybig.expansion |
a library providing the "data expansion functions" for effective data expansions |
tinybig.compression |
a library providing the "data compression functions" for effective data compression |
tinybig.transformation |
a library providing the "data transformation functions" for effective data transformation |
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.interdependence |
a library providing the "interdependence functions" for data interdependence relationships modeling |
tinybig.fusion |
a library providing the "fusionn functions" for multi-source/channel/head data integration |
tinybig.koala |
a library providing the functions from mathematics, statistics and other interdisciplinary sciences |
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.loss |
a library providing the loss functions for model introduced error computation in learning |
tinybig.metric |
a library providing the metrics that can be used for model performance evaluation |
tinybig.optimizer |
a library providing the optimizers that can be used for model parameter optimization in training |
tinybig.learner |
a library providing the learner that can be used for model effective and efficient training |
tinybig.visual |
a library of utility functions for data, model and learning process visualization and rendering |
tinybig.util |
a library of utility functions for RPN model design, implementation and learning |
tinybig.zootopia |
a library of models developed with the functions for concrete AI applications |
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).