Data compression using machine learning
How do you compress a ML model?
4 Key Techniques to Compress Machine Learning Models
- Quantisation
- Pruning
- Knowledge distillation
- Low-rank tensor decomposition
- Model compression reduces the size of a neural network (NN) without compromising accuracy.
This size reduction is important because bigger NNs are difficult to deploy on resource-constrained devices.
Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such as normalizing flows, variational autoencoders, diffusion probabilistic models, and generative adversarial
How can data compression reduce storage overhead?
To reduce storage overhead in these systems, data compression is widely adopted.
Most existing compression algorithms utilize the overall characteristics of the entire time series to achieve high compression ratio, but ignore local contexts around individual points.
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How do compression algorithms work?
Most existing compression algorithms utilize the overall characteristics of the entire time series to achieve high compression ratio, but ignore local contexts around individual points.
In this way, they are effective for certain data patterns, and may suffer inherent pattern changes in real-world time series.
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How does data compression affect the development of time series databases?
Abstract:
- The explosion of time series advances the development of time series databases
To
reduce storage overhead in these systems, data compression is widely adopted.
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What is lossless data compression using machine learning?
The proposed system comprises a method of lossless data compression using machine learning.
The model used is a sequence-to-sequence recurrent neural network (RNN) model for both compression and decompression.
The sequence-to-sequence model can predict sequence data seen in text and images.
What is data compression?
Data compression reduces the number of bits required to express the data in a compact format
It involves re-encoding the data using fewer bits than the actual representation
There are two major forms of data compression, namely “lossy data compression” and “lossless data compression”
What is lossless data compression using machine learning?
The proposed system comprises a method of lossless data compression using machine learning
The model used is a sequence-to-sequence recurrent neural network (RNN) model for both compression and decompression
The sequence-to-sequence model can predict sequence data seen in text and images
Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such as normalizing flows, variational autoencoders, diffusion probabilistic models, and generative adversarial networks.,While latent variable models can be designed to be complex density estimators
