Gradient Trajectory Data Selection for Neural Network Training
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Solution Overview
Problem
Existing methods for selecting training data for machine learning models are inefficient and lead to suboptimal performance due to individual sample rankings or inefficient matching processes, especially when scaling up, and do not effectively utilize gradient information for data selection.
Innovation Solution
A system that selects a subset of training examples by matching the gradient trajectories of a target data set through a pursuit process during warmup training, projecting gradients onto a small subspace for optimization, and de-duplicating examples to train models more robustly and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If top-k selection is used for data selection, then the process is fast and straightforward, but the performance is suboptimal compared to joint selection
Solution Approach 1:
The patent segments the data selection process into two phases: a fast top-k selection phase that identifies candidate examples based on individual gradient magnitudes, and a more precise joint selection phase that refines the selection by matching gradient trajectories across multiple examples. This segmentation allows the system to achieve both speed (from top-k) and precision (from joint selection) by combining their advantages in a structured manner.
Solution Approach 2:
The patent applies preliminary top-k selection to quickly identify and rank candidate training examples based on their individual gradient magnitudes before performing the more computationally intensive joint trajectory matching. This preliminary action filters out clearly inferior examples early in the process, reducing the search space for the subsequent joint selection and making the overall process more efficient while maintaining high selection quality.
2Device complexity
If individual sample rankings are used for data selection, then the process is simple, but the performance is suboptimal due to inefficient matching
Solution Approach 1:
The patent merges multiple individual sample rankings into a unified joint selection process that considers the gradient trajectories of multiple examples simultaneously. Instead of selecting examples based on their individual rankings alone, the system combines the ranking information with trajectory matching across examples, creating a more comprehensive selection criterion that achieves both simplicity and high performance by integrating multiple signals.
3Manufacturing precision
If gradient information is used for data selection, then model performance can be maximized, but computational and memory costs increase
Solution Approach 1:
The patent extracts and utilizes only the most critical aspect of gradient information - the trajectory patterns and their matching characteristics - rather than processing the complete gradient computations throughout the entire training process. By extracting trajectory information during warmup training and using it for selection, the system achieves high model performance while significantly reducing the computational burden during the main training phase.
Solution Approach 2:
The patent performs gradient trajectory computation and matching during a preliminary warmup training phase before the main training begins. This preliminary action completes the expensive gradient analysis work early when the model is still flexible, allowing the main training to proceed with a pre-computed selection of high-quality examples, thereby reducing overall computational costs while maintaining performance.
4Reliability
If joint data selection is performed, then model robustness is improved, but the process becomes computationally intensive and scales poorly
Solution Approach 1:
The patent segments the joint selection process into a warmup phase where gradient trajectories are computed and matched to identify high-quality examples, and a main training phase where the selected examples are used without re-computing trajectories. This segmentation enables the system to achieve robust joint selection for improved model reliability during warmup, while maintaining good scaling capability during the main training phase by avoiding redundant trajectory computations.
Data Source
AI summary
Systems, methods, and apparatus, including computer programs encoded on computer storage media for selecting, from a training data set of training examples, a subset of training examples that will be used for training a neural network by selecting the subset of training examples whose combined gradients over time match a target gradient trajectory.