Neural Network Layer Training with Associated Learning Losses
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Solution Overview
Problem
Existing speech processing systems face inefficiencies in training and deployment due to the reliance on end-to-end backpropagation, which can lead to computational resource constraints and robustness issues, particularly in devices with limited resources.
Innovation Solution
Implementing associated learning techniques that decompose end-to-end backpropagation into multiple optimization targets, using associated learning loss functions and stochastic terms to train neural network layers independently, allowing for parallel training and deployment of models with varying complexity based on available resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If end-to-end backpropagation is used for training neural networks, then training accuracy can be achieved, but computational resource constraints and training time increase significantly
Solution Approach 1:
The patent segments the end-to-end backpropagation process into multiple independent optimization targets corresponding to different neural network layers. Each layer is trained separately with its own loss function and gradients, allowing parallel training and reducing overall training time while maintaining accuracy.
Solution Approach 2:
The patent introduces dynamic training where different layers can be trained with different learning rates, optimization algorithms, and timing. This allows the system to adapt the training process dynamically, improving efficiency without sacrificing the precision needed for accurate model training.
2Measurement precision
If end-to-end backpropagation is used for training neural networks, then training accuracy can be achieved, but computational resource constraints worsen
Solution Approach 1:
By segmenting the training process into layer-independent optimizations, the patent reduces the computational burden on any single processing unit. Each layer can be trained with fewer computational resources simultaneously, lowering peak energy consumption while achieving the same overall training accuracy.
Solution Approach 2:
Each neural network layer performs self-service training by computing its own gradients and updating its parameters independently. This eliminates the need for complex coordinated computation across all layers, reducing overall computational resource requirements while maintaining training effectiveness.
3Adaptability or versatility
If fewer neural network layers are deployed on resource-constrained devices, then device compatibility improves, but model accuracy deteriorates
Solution Approach 1:
The patent enables independent training of neural network layers, allowing selective deployment of trained layers on resource-constrained devices. Even with fewer layers, each layer has been optimized independently with appropriate learning rates and loss functions, preserving accuracy while improving device compatibility.
4Productivity
If neural network layers are trained independently with associated learning, then training efficiency and robustness improve, but training complexity increases
Solution Approach 1:
The patent introduces associated learning loss functions as intermediaries that bridge independent layer training with overall model performance. These loss functions coordinate the independent training processes, managing complexity while enabling parallel training and improving efficiency.
Data Source
AI summary
A machine learning model may be configured for training using an associated learning technique. A model configured for end-to-end backpropagation may adapted for associated learning by introducing functions for projecting hidden vectors and labels to a shared representation space and for reconstructing labels from representation vectors. An associated learning loss may be calculated at each layer, with the resulting gradients backpropagated locally through that layer rather than all layers. A reconstruction loss may be calculated using each layer's output including the predicted label. Training by associated learning may be parallelized (e.g., layer by layer) to yield efficiency gains. In addition, associated learning training may be more robust to training label errors. The resulting model may be used to, for example, predict data sequences in an autoregressive manner in which subsequent portions of the output data sequence are predicted in part based on previous predicted portions of the output data sequence.