Factorized Neural Network Layer Spectral Initialization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex machine learning models require substantial computing resources for training and inference, leading to memory and computational burdens, and existing compression techniques often result in models that are no longer effective or fail to converge to similar solutions.
Innovation Solution
The technique involves factorizing neural network layers into the product of two or more matrices, using spectral initialization to maintain parameter scaling, and adapting the optimizer with a Frobenius decay function to reduce resource utilization during training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex neural network layers are used to maintain model performance, then model effectiveness is improved, but memory and computational resources required increase
Solution Approach 1:
The patent applies segmentation by factorizing a single complex weight matrix into multiple simpler matrices (e.g., W = A × B where A is m×r and B is r×n). This divides the original matrix multiplication operation into sequential multiplications with intermediate results, reducing peak memory requirements while maintaining the same computational functionality and model effectiveness.
2Device complexity
If model compression techniques are applied to reduce resource utilization, then memory and computational costs are reduced, but model effectiveness deteriorates or convergence fails
Solution Approach 1:
The patent employs parameter changes by modifying the initialization strategy for the factorized matrices. Specifically, it uses spectral initialization (based on singular value decomposition) to initialize the factor matrices A and B such that their product approximates the original weight matrix W. This careful parameter initialization ensures that the factorized model converges to similar solutions as the original model, maintaining effectiveness while achieving compression.
3Stability of the object's composition
If standard weight decay regularizer is used with factorized layers, then training stability is maintained, but parameter scaling is lost leading to poor convergence
Solution Approach 1:
The patent addresses this by changing the regularizer from standard weight decay to Frobenius norm-based decay. The Frobenius decay is applied to the factorized matrices with scaling factors that account for the dimensional changes introduced by factorization. This parameter change in the regularization approach maintains training stability while preserving the parameter scaling necessary for the factorized model to converge to similar solutions as the original model.
Data Source
AI summary
Aspects of the present disclosure relate to factorized neural network techniques. In examples, a layer of a machine learning model is factorized and initialized using spectral initialization. For example, an initial layer parameterized using an initial matrix is processed such that it is instead parameterized by the product of two or more matrices, thereby resulting in a factorized machine learning model. An optimizer associated with the machine learning model may also be processed to adapt a regularizer accordingly. For example, a regularizer using a weight decay function may be adapted to instead use a Frobenius decay function with respect to the factorized model layer. The factorized machine learning model may be trained using the processed optimizer and subsequently used to generate inferences.