Search Results for author: Miguel A. Carreira-Perpinan

Found 4 papers, 0 papers with code

Optimal Quantization Using Scaled Codebook

no code implementations CVPR 2021 Yerlan Idelbayev, Pavlo Molchanov, Maying Shen, Hongxu Yin, Miguel A. Carreira-Perpinan, Jose M. Alvarez

We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled.

Quantization

Structured Multi-Hashing for Model Compression

no code implementations CVPR 2020 Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel A. Carreira-Perpinan

Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.

Model Compression

A fast, universal algorithm to learn parametric nonlinear embeddings

no code implementations NeurIPS 2015 Miguel A. Carreira-Perpinan, Max Vladymyrov

This has two advantages: 1) The algorithm is universal in that a specific learning algorithm for any choice of embedding and mapping can be constructed by simply reusing existing algorithms for the embedding and for the mapping.

Dimensionality Reduction

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