Google llc (20240256865). TRAINING NEURAL NETWORKS USING LEARNED OPTIMIZERS simplified abstract

TRAINING NEURAL NETWORKS USING LEARNED OPTIMIZERS

Organization Name

google llc

Inventor(s)

Deepali Jain of Bangalore (IN)

Krzysztof Marcin Choromanski of Lincroft NJ (US)

Sumeet Singh of New York NY (US)

Vikas Sindhwani of Hastings-on-Hudson NY (US)

Tingnan Zhang of Sunnyvale CA (US)

Jie Tan of Mountain View CA (US)

Kumar Avinava Dubey of Palo Alto CA (US)

TRAINING NEURAL NETWORKS USING LEARNED OPTIMIZERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240256865 titled 'TRAINING NEURAL NETWORKS USING LEARNED OPTIMIZERS

Simplified Explanation: The patent application describes methods, systems, and apparatus for training neural networks using an optimizer neural network. This involves generating optimizer network inputs, processing them through the optimizer neural network, and applying the resulting updates to network parameters.

Key Features and Innovation:

• Training neural networks using an optimizer neural network
• Generating optimizer network inputs for each network parameter
• Processing optimizer network inputs to generate updates for network parameters

Potential Applications: This technology can be applied in various machine learning tasks, such as image recognition, natural language processing, and predictive analytics.

Problems Solved: This technology addresses the challenge of efficiently training neural networks by utilizing an optimizer neural network to generate updates for network parameters.

Benefits:

• Faster and more efficient training of neural networks
• Improved performance and accuracy in machine learning tasks
• Automated optimization of network parameters

Commercial Applications: Potential commercial applications include developing advanced AI systems for industries such as healthcare, finance, and autonomous vehicles. This technology can also be used in research institutions for cutting-edge machine learning projects.

Prior Art: Researchers can explore prior art related to optimizer neural networks, gradient optimization techniques, and neural network training methods to understand the evolution of this technology.

Frequently Updated Research: Stay updated on advancements in optimizer neural networks, gradient optimization algorithms, and novel approaches to training neural networks for the latest innovations in machine learning.

Questions about Neural Network Training with Optimizer Networks: 1. How does the use of an optimizer neural network improve the training process of neural networks? 2. What are the potential limitations or challenges associated with training neural networks using optimizer networks?

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks. one of the methods for training a neural network configured to perform a machine learning task includes performing, at each of a plurality of iterations: performing a training step to obtain respective new gradients of a loss function; for each network parameter: generating an optimizer network input; processing the optimizer network input using an optimizer neural network, wherein the processing comprises, for each cell: generating a cell input for the cell; and processing the cell input for the cell to generate a cell output, wherein the processing comprises: obtaining latent embeddings from the cell input; generating the cell output from the hidden state; and determining an update to the hidden state; and generating an optimizer network output defining an update for the network parameter; and applying the update to the network parameter.