Metadata-Version: 2.1
Name: pyscnn
Version: 0.0.3b0
Summary: Scalable Convex Neural Networks: a package for fasts convex optimization of shallow neural networks.
Author-email: Aaron Mishkin <amishkin@cs.stanford.edu>
License: MIT License
        
        Copyright (c) 2022 Pilanci Research Group
        
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Project-URL: Homepage, https://github.com/pilancilab/scnn
Project-URL: Bug Tracker, https://github.com/pilancilab/scnn/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# `scnn`: Scalable Convex Neural Networks 

A library for global optimization of shallow neural networks.
API documentation at [ReadTheDocs](https://scnn.readthedocs.io/en/latest/).

### Requirements

Python 3.8 or newer. Development dependencies are listed in `dev_requirements.txt`. 

### Setup

Install using `pip`:

```
python -m pip install pyscnn
```

Or, clone the repository and manually install: 

```
git clone https://github.com/pilancilab/scnn.git
python -m pip install scnn
```

### Contributions

Coming soon!

### Citation

Please cite our paper if you use this package.

```
@article{DBLP:journals/corr/abs-2202-01331,
  author    = {Aaron Mishkin and
               Arda Sahiner and
               Mert Pilanci},
  title     = {Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model
               Classes and Cone Decompositions},
  journal   = {CoRR},
  volume    = {abs/2202.01331},
  year      = {2022},
  url       = {https://arxiv.org/abs/2202.01331},
}
```

Looking for the code to replicate our experiments?
See [scnn_experiments](https://github.com/aaronpmishkin/scnn_experiments).
