https://github.com/AlexanderMath/fasth

Code for the article "What if Neural Networks had SVDs?", to be presented as a spotlight paper at NeurIPS 2020.
https://github.com/AlexanderMath/fasth

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Code for the article "What if Neural Networks had SVDs?", to be presented as a spotlight paper at NeurIPS 2020.

• Host: GitHub
• URL: https://github.com/AlexanderMath/fasth
• Owner: AlexanderMath
• License: mit
• Created: 2020-06-30T15:37:32.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-10-08T08:03:46.000Z (about 3 years ago)
• Last Synced: 2024-07-04T02:14:47.155Z (4 months ago)
• Language: Python
• Homepage:
• Size: 483 KB
• Stars: 68
• Watchers: 4
• Forks: 10
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        


# FastH

Code accompanying article What if Neural Networks had SVDs? accepted for spotlight presentation at NeurIPS 2020. 







**UPDATE:** We currently recommend using the newer fasth++ algorithm from fasthpp.py which only uses PyTorch (no need to compile CUDA code)!

If, for some reason, you want to use our CUDA code, please see this Google Colab. 

# Requirements 

First, check out how to run the code in Google Colab. 

To install locally, run 

```

pip install -r requirements.txt

```

Check installation by running test cases. 

```

python test_case.py

```

See test_case.py for expected output.

# Minimal Working Example 

```

import torch

from fasth_wrapper import Orthogonal 

class LinearSVD(torch.nn.Module): 

	def __init__(self, d, m=32): 

		super(LinearSVD, self).__init__()

		self.d		  = d

		self.U = Orthogonal(d, m)

		self.D = torch.empty(d, 1).uniform_(0.99, 1.01)

		self.V = Orthogonal(d, m)

	def forward(self, X):

		X = self.U(X)

		X = self.D * X 

		X = self.V(X)

		return X 

bs = 32

d  = 512

neuralSVD = LinearSVD(d=d)

neuralSVD.forward(torch.zeros(d, bs).normal_())

```

# Bibtex

If you use this code, please cite 

```

@inproceedings{fasth,

    title={What If Neural Networks had SVDs?,

    author={Mathiasen, Alexander and Hvilsh{\o}j, Frederik and J{\o}rgensen, Jakob R{\o}dsgaard and Nasery, Anshul and Mottin, Davide},

    booktitle={NeurIPS},

    year={2020}

}

```

A previous version of the article was presented at the ICML workshop on Invertible Neural Networks and Normalizing Flows. This does not constitute a dual submission because the workshop does not qualify as an archival peer reviewed venue.