Learned Differentiable Loss Functions for Data-Aligned Neural Networks
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Solution Overview
Problem
Conventional loss functions for machine learning models are predefined and not based on the training data, leading to performance limitations and inefficiencies, including increased processing cycles and misalignment with business objectives.
Innovation Solution
A learned loss function is generated by extracting values from a neural network's layers, training a surrogate model to output a symbolic equation, and applying this equation as a new loss function to improve model training, aligning it with the underlying data and business objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined loss functions are used for training neural networks, then the training process is simple and straightforward, but the model performance is limited and misaligned with business objectives
Solution Approach 1:
The loss function transitions from a static predefined mathematical form to a dynamic learned representation. The system learns loss function parameters or structures from data, allowing the loss function to adapt to specific business objectives and data characteristics rather than relying on fixed conventional forms.
Solution Approach 2:
The invention changes the parameters of the loss function from fixed predefined values to learned parameters. By training a separate model to predict loss function parameters or to directly learn the loss function form, the system optimizes these parameters to align with business goals, improving model performance.
2Measurement precision
If conventional predefined loss functions are used, then the training process requires fewer processing cycles, but the model accuracy and convergence are suboptimal
Solution Approach 1:
The system performs preliminary learning of the loss function parameters or form before the main model training process. By pre-learning the optimal loss function characteristics from data, the subsequent training process benefits from an optimized loss function that guides convergence more effectively, potentially reducing total processing cycles despite the initial learning phase.
Solution Approach 2:
The invention introduces a feedback mechanism where the learned loss function parameters are continuously refined based on model performance and business objective alignment. This feedback loop allows the system to adapt the loss function during training, improving accuracy while managing processing efficiency through iterative optimization.
3Adaptability or versatility
If learned loss functions are generated by training surrogate models and extracting symbolic equations, then model performance and alignment with business goals improve, but the training process complexity increases
Solution Approach 1:
The invention introduces a surrogate model as an intermediary between the training data and the loss function. This intermediate model learns the relationship between data characteristics and optimal loss function parameters, making the complex task of learning loss functions more manageable and systematic while improving adaptability to business objectives.
Solution Approach 2:
The training process is segmented into distinct phases: first training the surrogate model to learn loss function characteristics, then applying the learned loss function to the main model training. This segmentation allows each component to be optimized independently, managing overall complexity while achieving improved performance and adaptability.
Data Source
AI summary
Systems and methods are disclosed herein for training a model with a learned loss function. In an example system, a first trained neural network is generated based on application of a first loss function, such as a predefined loss function. A set of values is extracted from one or more of the layers of the neural network model, such as the weights of one of the layers. A separate machine learning model is trained using the set of values and a set of labels (e.g., ground truth annotations for a set of data). The machine learning model outputs a symbolic equation based on the training. The symbolic equation is applied to the first trained neural network to generate a second trained neural network. In this manner, a learned loss function can be generated and used to train a neural network, resulting in improved performance of the neural network.