# Compressive Clustering with an Optical Processing Unit

arXiv: https://arxiv.org/abs/2206.05928 · HTML: https://mlsanity.com/paper/2206.05928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Random Projections through multiple optical scattering: Approximating kernels at the speed of light](https://mlsanity.com/paper/1510.06664/) (2015)
- [Sketching for Large-Scale Learning of Mixture Models](https://mlsanity.com/paper/1606.02838/) (2016)
- [Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning](https://mlsanity.com/paper/2112.00423/) (2021)
- [Kernel computations from large-scale random features obtained by Optical Processing Units](https://mlsanity.com/paper/1910.09880/) (2019)
- [Fast Optical System Identification by Numerical Interferometry](https://mlsanity.com/paper/1911.01006/) (2019)

## Built on by (most cited first, 1 of 1)

- [Sketch and shift: a robust decoder for compressive clustering](https://mlsanity.com/paper/2312.09940/) (2023)

## Abstract (arXiv)

We explore the use of Optical Processing Units (OPU) to compute random Fourier features for sketching, and adapt the overall compressive clustering pipeline to this setting. We also propose some tools to help tuning a critical hyper-parameter of compressive clustering.