At the 33rd conference of the Association for the Advancement of Artificial Intelligence (AAAI), Amazon Alexa researchers in collaboration with researchers from University of Texas will be presenting paper that describes a new method for compressing neural networks which will, in turn, increase the performance of the network.
Yesterday, on the Amazon Blog, Anish Acharya and Rahul Goel, both applied scientists at Amazon Alexa AI, explained how huge neural networks tend to slow down the performance of a system. The proposed paper called ‘”Online Embedding Compression for Text Classification using Low Rank Matrix Factorization”, includes a method to compress embedding tables that often compromises the NLU network’s performance thus slowing down AI based systems like Alexa. This will help Alexa perform more and more complex tasks in milliseconds.
The researchers covered the following topics within the paper:
The blog lists in short, the steps taken by the researchers to compress the neural network:
The system developed by the researchers could shrink a neural network by 90 percent for both LSTM and DAN models, while reducing its accuracy by less than 1%. They compared their model to two alternatives. One in which the embedding table is compressed before network training begins and the other is simple quantization, in which all of the values in the embedding vector are rounded to a limited number of reference values. On testing their approach across a range of compression rates, on different types of neural networks, using different data sets, they found that their system outperformed the other approaches used in the experiment.
You can read the research paper for more details on the experiments and acquired results.
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