**arXiv ID:** 1412.3714 **Authors:** Jiwei Li **Published:** 2014-12-11T16:35:27Z **Abstract:** This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the ...
Scanned 9/11/2026
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# Feature Weight Tuning for Recursive Neural Networks
**arXiv ID:** 1412.3714
**Authors:** Jiwei Li
**Published:** 2014-12-11T16:35:27Z
**Abstract:**
This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the higher-level representation. The proposed model can be viewed as incorporating a more powerful compositional function for embedding acquisition in recursive neural networks. Experimental results demonstrate the significant improvement over standard neural models.
## Skill Description
This skill is generated from the arXiv paper: Feature Weight Tuning for Recursive Neural Networks (1412.3714).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:1412.3714](http://arxiv.org/abs/1412.3714v2)
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