Neural Adapter Translation for End-to-End ML Pipeline Training
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Solution Overview
Problem
Classical machine learning (ML) pipelines are trained sequentially in a greedy fashion, leading to sub-optimal performance due to disjointed training of operators, which does not result in joint optimization, while neural networks (NNs) are trained end-to-end, leveraging global parameter estimation and parallelization benefits.
Innovation Solution
A framework is developed to translate classical ML pipelines into neural networks (NNs) by determining operator dependencies, selecting corresponding NN modules, and wiring them according to these dependencies, enabling end-to-end training and tuning using backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sequential training of ML pipeline operators is used, then training simplicity and toolset maturity are maintained, but model accuracy and optimization performance deteriorate due to isolated training without joint optimization
Solution Approach 1:
The patent introduces an adapter layer as an intermediary component between the ML pipeline operators and the neural network framework. This adapter enables the ML pipeline to be trained using end-to-end neural network training methods (with joint optimization) while maintaining compatibility with classical ML operators. The adapter translates ML operator outputs into neural network compatible formats, allowing gradient flow and backpropagation through the entire pipeline, thus achieving both training simplicity through unified framework and improved model accuracy through joint optimization.
2Adaptability or versatility
If classical ML pipelines are used, then familiarity and toolset maturity are preserved, but training efficiency and runtime performance worsen due to greedy sequential training approach
Solution Approach 1:
The patent substitutes the mechanical sequential training mechanism with a neural network-based end-to-end training mechanism. By wrapping ML pipeline operators in adapter layers that support gradient computation and backpropagation, the system replaces the greedy sequential training approach with parallelizable neural network training methods. This allows the use of mature ML toolsets while achieving the training efficiency and performance benefits of neural network frameworks through techniques like automatic differentiation and distributed training.
3Manufacturing precision
If end-to-end neural network training is applied to ML pipelines, then joint optimization and model performance improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the ML pipeline into individual operators, each wrapped with its own adapter layer. This segmentation allows the complex end-to-end training process to be broken down into manageable components. Each adapter handles the specific translation and gradient computation for its corresponding ML operator, making the overall system more tractable. The segmentation approach maintains clear boundaries between components while enabling joint optimization through the unified neural network training framework.
Data Source
AI summary
Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training. Some examples determine whether an operator has multiple corresponding NN modules within the translation dictionary and make an optimized selection.