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Compressive Clustering with an Optical Processing Unit
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.
Sparse matrix products for neural network compression
Over-parameterization of neural networks is a well known issue that comes along with their great performance.
Deep Networks with Adaptive Nyström Approximation
Recent work has focused on combining kernel methods and deep learning to exploit the best of the two approaches.
QuicK-means: Acceleration of K-means by learning a fast transform
K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be.
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