Regularizing Recurrent Neural Networks via Latent State Divergence
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Solution Overview
Problem
Recurrent machine-learned models, such as RNNs and LSTMs, face challenges in training due to overfitting and loss of contextual information as complexity increases, making it difficult to retain important long-term information for generating predictions.
Innovation Solution
The system trains recurrent machine-learned models by determining both a latent distribution and a prior distribution for the latent state, using a divergence loss to penalize significant deviations between them, which prevents overfitting and encourages simpler, smoother transitions between latent states, thereby retaining additional contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the complexity and size of the recurrent machine-learned model increases, then the model's capacity to capture contextual information improves, but the model becomes prone to overfitting and loses important contextual information
Solution Approach 1:
The patent applies parameter changes by introducing a regularization term (divergence loss) that modifies the training objective function. This regularization term controls the deviation of latent states from their prior distribution, effectively constraining the model parameters to prevent overfitting while maintaining the model's capacity to capture contextual information through the recurrent architecture.
2Reliability
If regularization approaches are applied to reduce model complexity, then overfitting is reduced, but training recurrent machine-learned models to retain important contextual information becomes challenging
Solution Approach 1:
The patent implements feedback through the divergence loss term, which continuously monitors and penalizes deviations of the current latent state from the prior distribution. This feedback mechanism guides the training process by providing gradient information that adjusts the model parameters to maintain smooth transitions between latent states, thereby retaining contextual information while preventing overfitting.
3Measurement precision
If the model retains more contextual information through increased complexity, then prediction accuracy improves, but training time increases
Solution Approach 1:
The patent modifies the training objective by adding a regularization term that constrains latent state deviations. This parameter change in the loss function enables the model to learn smoother latent state transitions, which reduces the effective search space during training and accelerates convergence, thereby reducing training time while maintaining prediction accuracy.
Data Source
AI summary
A modeling system trains a recurrent machine-learned model by determining a latent distribution and a prior distribution for a latent state. The parameters of the model are trained based on a divergence loss that penalizes significant deviations between the latent distribution the prior distribution. The latent distribution for a current observation is a distribution for the latent state given a value of the current observation and the latent state for the previous observation. The prior distribution for a current observation is a distribution for the latent state given the latent state for the previous observation independent of the value of the current observation, and represents a belief about the latent state before input evidence is taken into account.