Adaptive Sampling for Robust Model Fitting
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Solution Overview
Problem
Existing model fitting techniques face challenges in efficiently determining optimal batch sizes for second-order methods, leading to oscillating behaviors and inferior performance, especially when the algorithm or training set changes, which affects reusability and flexibility.
Innovation Solution
A method that automatically determines optimized target parameters by selecting a sample size, damping factor, and iteration value, sampling training data, and updating performance parameters based on generalization error analysis to achieve robust and flexible model fitting across various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning of batch size is performed for second-order methods, then optimization performance can be improved, but the process becomes time-consuming and inflexible when algorithms or training sets change
Solution Approach 1:
The system automatically determines optimized target parameters for second-order methods by monitoring performance and adapting batch sizes without manual intervention. The algorithm self-adjusts by evaluating performance metrics and selecting appropriate parameters, eliminating the need for time-consuming manual tuning while maintaining optimal optimization performance.
2Device complexity
If fixed batch size is used for second-order methods, then implementation is simple, but performance oscillates and is inferior when training set or algorithm changes
Solution Approach 1:
The patent implements dynamic batch size adjustment for second-order methods by continuously monitoring performance metrics and adapting the batch size accordingly. Instead of using a fixed batch size, the system dynamically selects batch sizes based on current training state and performance feedback, ensuring stable and reliable optimization performance even when training sets or algorithms change.
3Measurement precision
If large sample size is used for model fitting, then model accuracy improves, but training time increases significantly
Solution Approach 1:
The system optimizes the trade-off between model accuracy and training time by dynamically adjusting the sample size parameter during training. Based on performance monitoring and adaptive sampling strategies, the system determines the optimal sample size at different training stages, achieving high model accuracy without the linear increase in training time that would result from always using large sample sizes.
Data Source
AI summary
Aspects of the present disclosure relate generally to model fitting. A target model having a large number of inputs is fit using a performance model having relatively few inputs. The performance model is learned during the fitting process. Optimal optimization parameters including a sample size, a damping factor, and an iteration count are selected for an optimization round. A random subset of data is sampled based on the selected sample size. The optimization round is conducted using the iteration count and the sampled data to produce optimized parameters. The performance model is updated based on the performance of the optimization round. The parameters of the target model are then updated based on the damping factor and the parameters computed by the optimization round. The aforementioned steps are performed in a loop in order to obtain optimized parameters and fit of the data to the target model.