Compression of Machine-Learned Models via Entropy Penalized Weight Reparameterization

Organization Name

Inventor(s)

Abhinav Shrivastava of Silver Springs MD (US)

Compression of Machine-Learned Models via Entropy Penalized Weight Reparameterization - A simplified explanation of the abstract

This abstract first appeared for US patent application 18409520 titled 'Compression of Machine-Learned Models via Entropy Penalized Weight Reparameterization

Simplified Explanation: The patent application describes a method for compressing machine-learned models by representing the model parameters in a reparameterization space during training.

The approach involves learning a compressed representation of the model using a latent-variable data compression method.
Model parameters such as weights and biases are represented in a reparameterization space, allowing for compression.
A learned probability model is used to impose an entropy penalty during training and compress the representation using arithmetic coding after training.
The method aims to maximize accuracy and compressibility of the model jointly, with a specified rate-error trade-off controlled by a hyperparameter.

Potential Applications: 1. Data compression in machine learning models. 2. Efficient storage and deployment of complex neural networks. 3. Improving the scalability of machine learning algorithms.

Problems Solved: 1. Addressing the challenge of model size and complexity in machine learning. 2. Enhancing the efficiency of model training and deployment. 3. Balancing accuracy and compressibility in machine-learned models.

Benefits: 1. Reduced storage requirements for machine learning models. 2. Improved performance and speed of model deployment. 3. Enhanced scalability and flexibility in machine learning applications.

Commercial Applications: The technology could be applied in industries such as data analytics, artificial intelligence, and IoT for optimizing model storage and deployment, leading to cost savings and improved efficiency.

Prior Art: Readers interested in prior art related to this technology may explore research papers on model compression techniques in machine learning and neural networks.

Frequently Updated Research: Stay updated on advancements in model compression techniques, probabilistic modeling in machine learning, and data compression methods for neural networks.

Questions about Model Compression: 1. How does the proposed method compare to existing techniques for model compression? 2. What are the potential limitations or challenges of implementing this approach in real-world applications?

Original Abstract Submitted

Example aspects of the present disclosure are directed to systems and methods that learn a compressed representation of a machine-learned model (e.g., neural network) via representation of the model parameters within a reparameterization space during training of the model. In particular, the present disclosure describes an end-to-end model weight compression approach that employs a latent-variable data compression method. The model parameters (e.g., weights and biases) are represented in a “latent” or “reparameterization” space, amounting to a reparameterization. In some implementations, this space can be equipped with a learned probability model, which is used first to impose an entropy penalty on the parameter representation during training, and second to compress the representation using arithmetic coding after training. The proposed approach can thus maximize accuracy and model compressibility jointly, in an end-to-end fashion, with the rate-error trade-off specified by a hyperparameter.