Bloom Filter Feature Encoding for Neural Network Training
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Solution Overview
Problem
Training deep neural networks (DNNs) with large numbers of sparse and discrete features is inefficient due to high computation and storage costs, leading to slow convergence and lower accuracy, especially when dealing with vast amounts of data in information retrieval tasks.
Innovation Solution
The use of a bloom filter to reduce the dimensionality of sparse features, condensing them into a lower-dimensional space, thereby reducing the number of weights and processing resources required, which leads to faster convergence and higher accuracy in model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used to process large numbers of sparse and discrete features, then the model can capture complex patterns in information retrieval data, but the computation and storage costs become enormous, leading to slow convergence and lower accuracy
Solution Approach 1:
The patent segments the processing pipeline into two distinct stages: (1) a lookup table stage that handles sparse feature encoding with O(1) complexity, and (2) a neural network stage that processes only the encoded features. This segmentation separates the burden of handling sparsity from the neural network, allowing the model to maintain high accuracy while dramatically reducing computation and storage requirements.
Solution Approach 2:
The patent transforms the feature space by introducing an encoding dimension through lookup tables. Instead of directly processing high-dimensional sparse features in the neural network, the features are first mapped to a lower-dimensional encoded representation. This dimensional transformation reduces the input size to the neural network while preserving the essential information needed for accurate predictions.
2Adaptability or versatility
If the number of input nodes increases to handle more features, then the model can process more information, but the processing resources and time required become enormous
Solution Approach 1:
The patent performs preliminary encoding of sparse features using lookup tables before the features enter the neural network. This pre-processing step converts high-dimensional sparse features into compact encoded representations, reducing the number of input nodes the neural network must handle. As a result, the model maintains its ability to process diverse features while significantly improving training efficiency and reducing computational overhead.
Data Source
AI summary
According to one embodiment, a first set of features is received, where each of the features in the first set being associated with a predetermined category. A bloom filter is applied to the first set of features to generate a second set of features. A neural network model is trained by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection. The output of the neural network model is compared with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition.