Differentiable Estimator Subnetwork for DNN Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Integrating deep neural networks (DNNs) with external applications through APIs is challenging due to the requirement for all solution parts to be differentiable, limiting the ability to train DNNs end-to-end when interacting with non-differentiable external software applications.
Innovation Solution
A differentiable estimator subnetwork is operated during end-to-end training to estimate the functionality of external software applications, allowing API communication between the DNN and the external application, and enabling the DNN to learn and replace the estimator subnetwork with the actual application functionality during inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a DNN is trained to interact with an external application through its API, then the DNN can leverage existing analytic techniques and closed algorithms, but the training process becomes difficult because most DNN training procedures rely on gradient backpropagation which requires all solution parts to be differentiable
Solution Approach 1:
The patent introduces a differentiable estimator subnetwork as an intermediary component that bridges the DNN and the external non-differentiable application. During training, the estimator subnetwork approximates the external application's functionality in a differentiable manner, enabling gradient flow through the entire system. This mediator allows end-to-end training while accommodating the non-differentiable nature of external applications, resolving the contradiction between leveraging external analytics and maintaining trainability.
Solution Approach 2:
The patent creates a differentiable estimator subnetwork that copies or approximates the functionality of the external non-differentiable application during training. This estimator subnetwork learns to replicate the external application's behavior using differentiable operations, allowing the DNN to train end-to-end. During inference, the estimator is replaced with the actual external application, maintaining the desired functionality while enabling efficient training.
2Extent of automation
If all parts of the overall solution must be differentiable for end-to-end training, then gradient backpropagation can be applied, but this limits the ability to integrate non-differentiable external software applications
Solution Approach 1:
The differentiable estimator subnetwork serves as a mediator that enables end-to-end training with non-differentiable external applications. It translates the non-differentiable external application calls into differentiable operations during training, maintaining automation while expanding integration capability to accommodate diverse external systems.
Solution Approach 2:
The patent implements a dynamic system where the estimator subnetwork is replaced with the actual external application during inference. This dynamic switching allows the system to adapt to different external applications while maintaining consistent training procedures, enhancing both automation and versatility.
3Adaptability or versatility
If external applications are integrated into the DNN, then functionalities or logic provided by external applications can be included in the DNN, but the integration process becomes complex due to the need for handling different data representations and API communication
Solution Approach 1:
The differentiable estimator subnetwork acts as an intermediary layer that handles data representation transformations and API communication protocols. It abstracts the complexity of interacting with external applications, providing a standardized interface that simplifies integration while enabling functional extension.
Solution Approach 2:
The estimator subnetwork is designed to be universal, capable of handling different data representations and external application interfaces through a common differentiable framework. This multi-functional design reduces integration complexity by providing a single approach that works across diverse external applications.
Data Source
AI summary
During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.