Indirect Network for Adaptive Neural Regularization

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Solution Overview

Problem

Conventional regularization techniques for neural networks are limited by their global application, ignoring structural dependencies among weights, and lack adaptability to data properties, leading to suboptimal weight learning and generalization.

Innovation Solution

The introduction of an indirect network that learns an expected weight distribution for a direct network, allowing for adaptive regularization that accounts for structural dependencies and data-specific characteristics, thereby improving weight learning and generalization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional global regularization techniques are applied to neural networks, then the training process becomes simpler and more uniform, but the model's ability to capture structural dependencies among weights deteriorates, leading to suboptimal generalization

Engineering Contradiction:

Improveregularization application simplicityVSAvoidgeneralization performance

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent segments the regularization process by introducing an indirect network that separately learns the expected weight distribution, while the direct network focuses on the primary mapping task. This segmentation allows each network to specialize: the indirect network captures structural dependencies through weight distribution learning, while the direct network maintains operational simplicity for the main task.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The indirect network serves as an intermediary between the training data and the direct network weights. It mediates by learning the expected weight distribution that encapsulates structural dependencies and data properties, then uses this distribution to regularize the direct network, thereby improving generalization without complicating the direct network's operation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If standard regularization methods are used, then the training process is faster and requires fewer computational resources, but the adaptability to data-specific properties and structural dependencies is lost

Engineering Contradiction:

Improvetraining speedVSAvoidadaptability to data properties

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent introduces dynamics by making the regularization adaptive rather than static. The indirect network dynamically learns the expected weight distribution specific to each dataset and task, allowing the regularization to adapt to data properties. This dynamic adaptation occurs during training without requiring manual tuning or complex preprocessing.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The indirect network performs preliminary action by learning the expected weight distribution before the direct network uses it for regularization. This preliminary learning of structural dependencies and data-specific characteristics prepares the regularization mechanism, enabling the direct network to train efficiently with adaptive constraints already in place.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If an indirect network is introduced to learn expected weight distribution, then the regularization becomes more adaptive and accurate, but the overall system complexity increases

Engineering Contradiction:

Improveweight distribution accuracyVSAvoidnetwork architecture complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The indirect network is designed with multi-functionality: it learns the expected weight distribution, captures structural dependencies, adapts to data properties, and provides regularization signals. This universal design allows a single additional network to perform multiple functions that would otherwise require separate mechanisms, thereby limiting the increase in overall system complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The indirect network is nested within the overall training framework of the direct network. Rather than being a completely separate system, the indirect network is integrated into the training process, with its learned weight distribution directly informing the regularization of the direct network. This nesting allows the complexity to be organized hierarchically, with the indirect network serving the direct network.

Inventive Principle:

Principle #7Nested doll (Nesting)

Data Source

PatentUS20250139436A1Intelligent regularization of neural network architectures

Publication Date: 2025.05.01 UBER TECHNOLOGIES INC

AI summary

A system or method for training neural networks using an indirect network. The system receives a set of direct inputs and provides them to a direct network with a set of weights. An indirect network generates a distribution of expected weights for each weight based on indirect parameters. If a direct input includes missing data, the indirect network modifies the distribution to reduce reliance on the incomplete input. Initial weight values are set using the modified distributions, and training input is processed to generate training output. The system determines an error between the expected and training outputs, updating the indirect network's parameters and generating updated distributions of expected weights. The direct network's weights are further updated based on the error and updated distributions. Moreover, the trained direct network generates outputs using the updated weights.