Removing Biased Gradients from Memory for Streaming Data
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Solution Overview
Problem
Machine learning models face challenges in handling nonstationary streaming time-series data due to biased gradients, which affect the accuracy and efficiency of model training, particularly in adapting to changing data distributions without human intervention.
Innovation Solution
A system and method that utilize a gradient descent method to generate a parameter sequence by updating memory based on prior iteration counts, adapting memory size to remove biased gradients, and tuning the learning rate to minimize objective functions, enabling the learning of time-series models in an online manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If memory size is increased to store more gradient information for better model training, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts and removes biased gradients from the memory storage. By identifying and eliminating the biased portion of gradient information, the system maintains accurate curvature and moment estimates without requiring increased memory capacity. This extraction principle directly resolves the contradiction by improving measurement precision through selective removal of harmful data elements rather than through brute-force memory expansion.
Solution Approach 2:
The patent changes the parameter composition within memory by transforming gradient information to remove bias. Instead of storing raw gradient data that accumulates bias over time, the system applies parameter transformation to eliminate the biased component while preserving the useful signal. This allows accurate estimates with limited memory resources.
2Manufacturing precision
If traditional gradient descent methods are used without bias removal, then device complexity remains low, but manufacturing precision deteriorates due to biased gradients affecting model training accuracy
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors gradient information for bias indicators and dynamically adjusts memory content accordingly. By detecting biased gradients and triggering removal operations, the system maintains high model training accuracy through adaptive feedback control, justifying the additional device complexity through sustained precision improvements.
Solution Approach 2:
The memory adaptation mechanism serves itself by automatically identifying and removing biased gradients without external intervention. The system self-regulates its own data quality, performing precision maintenance autonomously. This self-service capability justifies the added complexity by eliminating the need for external quality control mechanisms.
3Loss of information
If memory is not adapted to remove biased gradients, then ease of operation is maintained, but loss of information occurs due to biased gradients degrading model performance
Solution Approach 1:
The patent applies preliminary action by proactively removing biased gradients before they can significantly degrade model performance. Instead of waiting for bias accumulation to cause substantial information loss, the system performs preventive removal operations, thereby preserving information integrity while maintaining operational simplicity through automated early intervention.
Data Source
AI summary
A computer-implemented method for implementing a computer system task involving streaming data by removing biased gradients from memory includes generating a parameter sequence including a plurality of parameters corresponding to respective iteration counts. Generating the parameter sequence includes obtaining a first parameter value corresponding to a given iteration count by updating memory corresponding to the given iteration count based on a second parameter value corresponding to a prior iteration count, adapting a size of the updated memory to remove biased gradients, and obtaining the first parameter value by performing a step of a gradient descent method based on the adaptation and the second parameter value. The method further includes learning a time-series model based on the parameter sequence, and implementing a computer system task using the time-series model.