Re: [AIRG] Model Compression -- Binarized Neural Network

[Aubrey Barnard <barnard@xxxxxxxxxxx>]

AIRG,

I would like to remind you all that later today Xianda (Bryce) Xu will 
be presenting on binarized deep neural networks, which are more 
efficient in terms of speed and memory than regular ones while still 
performing just as (or more) accurately.

4pm, CS 3310
https://arxiv.org/abs/1602.02830

See you there!

Aubrey


On 10/31/18 6:37 PM, Bryce XU via AIRG wrote:
> Hi, everyone!
> 
> 
> I am a visiting Junior undergraduate from University of Electronic 
> Science and Technology of China.
> 
> 
> Next Wednesday ( 7th Nov. ), I would like to share you guys with 
> something about Model Compression.
> 
> 
> I will first talk about the significance of model compression in 
> artificial intelligence and possible solutions. Then, I will show you 
> how to binarize the model in order to compress and speed it up. 
> Afterwards, I will talk a little bit about my experiments and some 
> problems we may encounter in binarizing the model. Last, some recent 
> work will be covered.
> 
> 
> The paper you need to read is https://arxiv.org/abs/1602.02830.
> 
> 
> I will also give you some optional papers if you are interested in this 
> topic.
> 
> http://papers.nips.cc/paper/5647-binaryconnect-training-deep-neural-networks-with-b
> https://link.springer.com/chapter/10.1007/978-3-319-46493-0_32
> 
> https://arxiv.org/abs/1612.01064
> 
> https://arxiv.org/abs/1806.07550
> 
> 
> Best,
> 
> 
> Xianda (Bryce) Xu
> 
> [1602.02830] Binarized Neural Networks: Training Deep ... 
> <https://arxiv.org/abs/1602.02830>
> arxiv.org
> Abstract: We introduce a method to train Binarized Neural Networks 
> (BNNs) - neural networks with binary weights and activations at 
> run-time. At training-time the binary weights and activations are used 
> for computing the parameters gradients. During the forward pass, BNNs 
> drastically reduce memory size and accesses, and replace most arithmetic 
> operations with bit-wise operations, which is ...
> 
> 
> 
> 
> 
> 
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