Adaptive Pooling Hyperparameters for Variable-Length Text Inputs
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Solution Overview
Problem
Existing adaptive pooling algorithms for neural networks in natural language processing face inefficiencies when dealing with variably sized text documents, leading to over-compression of smaller documents, loss of semantic information, and increased training time due to padding and window overlap.
Innovation Solution
Implementing naïve and optimized adaptive pooling algorithms that determine window size and stride length based on input and output sizes, minimizing window overlap and padding, using integer linear programming to optimize window size and stride length pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed window size and stride length are used in pooling layers, then computational efficiency is improved, but semantic information is lost when inputs have variable size
Solution Approach 1:
The patent implements adaptive pooling layers that dynamically adjust window size and stride length based on input characteristics. The system computes optimal hyperparameters for each input sequence length, allowing the pooling operation to adapt its behavior rather than using fixed parameters. This dynamic adjustment preserves semantic information for variable-sized inputs while maintaining computational efficiency through automated hyperparameter selection.
2Loss of information
If adaptive pooling is implemented to handle variable input sizes, then semantic information is preserved, but computational latency increases
Solution Approach 1:
The system employs self-service mechanisms where the adaptive pooling layer automatically determines its own optimal hyperparameters based on the input characteristics. The hyperparameter selection process is integrated into the forward pass, allowing the layer to adapt to each input without requiring external configuration or manual tuning. This automation preserves semantic information while minimizing the overhead of adaptive computation.
3Productivity
If window size is increased to reduce the number of pooling operations, then computational speed is improved, but window overlap increases causing redundancy
Solution Approach 1:
The patent systematically varies window size and stride length parameters to find optimal configurations for different input sizes. By changing these parameters adaptively rather than using fixed values, the system achieves computational efficiency with larger windows while avoiding excessive overlap through optimized stride length selection. The hyperparameter optimization process identifies parameter combinations that balance computational speed with minimal redundancy.
Data Source
AI summary
Adaptive pooling layers for compressing variably sized inputs use window sizes and stride lengths specific to variable input size and fixed output size at the pooling layer. A naïve and an optimal adaptive pooling algorithm disclosed herein determine window size and stride length for variable sized inputs while minimizing window size and ensuring no padding is used in the output representation. These adaptive pooling algorithms are implemented in a pipeline for text document classification involving a natural language processor that generates embedding vectors for variably sized text documents and at least one of the adaptive pooling algorithms at a first adaptive pooling layer of a classification neural network to process the embedding vectors.