Search Results for author: Yerlan Idelbayev

Found 7 papers, 5 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

A flexible, extensible software framework for model compression based on the LC algorithm

1 code implementation15 May 2020 Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with minimal effort.

Low-rank compression Model Compression +2

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

Model compression as constrained optimization, with application to neural nets. Part II: quantization

1 code implementation13 Jul 2017 Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev

We consider the problem of deep neural net compression by quantization: given a large, reference net, we want to quantize its real-valued weights using a codebook with $K$ entries so that the training loss of the quantized net is minimal.

Binarization Model Compression +1

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