Neural Network Sparsity via L0 Norm Approximation
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Solution Overview
Problem
Current neural network technologies face challenges in achieving sufficient sparsity for document search using inverted indices due to the inability to directly calculate the differentiable L0 norm, leading to potential search omissions from vocabulary ambiguity and notation variations.
Innovation Solution
A learning apparatus that generates features for search queries and documents using a neural network, with an update mechanism that employs a differentiable approximation function of the L0 norm to stabilize vector sparsity, enabling effective document search through an inverted index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If L1 norm approximation is used to evaluate sparsity in neural network vectors, then differentiability is maintained for gradient-based optimization, but the vector cannot acquire sufficient sparsity
Solution Approach 1:
The patent introduces an intermediary mechanism by adding a sparsity evaluation term based on L0 norm approximation to the loss function. This intermediary term acts as a bridge between the differentiable L1 norm and the non-differentiable L0 norm, allowing the system to maintain gradient-based optimization while guiding the vector toward sufficient sparsity through the intermediary sparsity evaluation component.
2Measurement precision
If L0 norm is directly calculated to evaluate sparsity, then accurate sparsity measurement is achieved, but differentiability is lost making gradient-based optimization impossible
Solution Approach 1:
The patent uses an intermediary approach by incorporating a sparsity evaluation term that approximates L0 norm properties while maintaining differentiability. This intermediary term enables the system to achieve accurate sparsity measurement without losing the ability to perform gradient-based optimization, as the intermediary formulation preserves the computational tractability required for neural network training.
Solution Approach 2:
The patent changes the parameter formulation of the sparsity evaluation by using a differentiable approximation function that mimics L0 norm behavior. By transforming the non-differentiable L0 norm calculation into a differentiable parameter form that can be integrated into the loss function, the system achieves both accurate sparsity measurement and optimization feasibility simultaneously.
Data Source
AI summary
A learning apparatus according to an embodiment has a feature generation means configured to take a search query, a first document related to the search query, and a second document that is not related to the search query as input, and generate a feature of the search query, a feature of the first document, and a feature of the second document, by using model parameters of a neural network, and an update means configured to take the feature of the search query, the feature of the first document, and the feature of the second document as input, and update the model parameters by using an error function including a cost function that is a differentiable approximation function of an L0 norm.