Differentiable Decision Tree Training via Continuous Parameter Vectors
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Solution Overview
Problem
Existing machine learning models, such as decision trees, are inefficient for training due to their non-differentiable nature, which prevents the use of gradient-based training methods, especially in devices with limited data processing resources.
Innovation Solution
A method for training a machine learning model that involves representing training sensor data as input vectors, processing these vectors through a sequence of decisions in a decision tree, and adjusting parameter vectors within a continuous value range to reduce total loss, making gradient-based training possible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a decision tree is used for object detection in devices with limited data processing resources, then the model complexity is reduced and ease of operation is improved, but the training efficiency deteriorates because decision trees are non-differentiable and gradient-based training methods cannot be applied
Solution Approach 1:
The patent transforms the non-differentiable decision tree into a differentiable form by changing the parameter representation. Instead of discrete component selections, the patent uses continuous parameter vectors where each component represents a feature weight. The decision function becomes a differentiable operation computing the dot product between the input vector and parameter vector, allowing gradient-based optimization while maintaining the decision tree's interpretability and low computational complexity.
2Device complexity
If a simple machine learning model like a decision tree is used in devices with limited data processing resources, then the device complexity is reduced, but the adaptability deteriorates because the model cannot be efficiently retrained on new data
Solution Approach 1:
By transforming the decision tree into a differentiable form with continuous parameter vectors, the patent enables efficient retraining on new data using gradient-based methods. The parameter vectors can be adjusted continuously to adapt to new data distributions while maintaining the simple structural form that keeps device complexity low. This allows the model to be updated incrementally without requiring complex retraining infrastructure.
Solution Approach 2:
The patent makes the decision tree dynamic by allowing its parameters to be continuously adjusted through gradient-based optimization. The parameter vectors can adapt to new data patterns, making the model flexible and versatile. This dynamic capability enables the simple model structure to evolve and adapt to changing requirements while maintaining computational efficiency.
3Ease of manufacture
If discrete component selection is used in decision tree nodes, then the model remains interpretable and simple, but the differentiability deteriorates making gradient-based training impossible
Solution Approach 1:
The patent resolves this contradiction by changing from discrete component selection to continuous parameter vectors. Each parameter vector component corresponds to a feature dimension, and the decision is made by computing the dot product and comparing with a threshold. This continuous parameterization maintains the interpretability of which features are important (through the parameter vector structure) while enabling differentiability for gradient-based training.
Data Source
AI summary
A method for training a machine learning model to classify sensor data. The method includes, for each training sensor data element of a plurality of training sensor data elements, processing a relevant input vector through a sequence of decisions of the machine learning model, wherein, for each decision, the scalar product of the input vector with a relevant parameter vector is formed and the result of the decision depends on whether the scalar product is less or greater than a specified relevant parameter; ascertaining a loss for the training data element; and adjusting the machine learning model to reduce a total loss, which includes the losses ascertained for the sensor data training data elements, wherein the parameter vector for each decision of the machine learning model is adjusted within a continuous value range.