# The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains

arXiv: https://arxiv.org/abs/2309.12279 · HTML: https://mlsanity.com/paper/2309.12279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [On weight initialization in deep neural networks](https://mlsanity.com/paper/1704.08863/) (2017)
- [ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection](https://mlsanity.com/paper/1906.01155/) (2019)
- [DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data](https://mlsanity.com/paper/2104.11629/) (2021)
- [Feature Imitating Networks](https://mlsanity.com/paper/2110.04831/) (2021)
- [MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs](https://mlsanity.com/paper/2210.17060/) (2022)
- [A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora](https://mlsanity.com/paper/2211.09956/) (2022)
- [Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks](https://mlsanity.com/paper/2306.14572/) (2023)

## Built on by (most cited first, 1 of 1)

- [Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study](https://mlsanity.com/paper/2402.09464/) (2024)

## Abstract (arXiv)

Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to approximate specific closed-form statistical features, setting a promising foundation for deep learning architectures. While the applicability of FINs has been chiefly tested in biomedical domains, this study extends its exploration into other time series datasets. Three different experiments are conducted in this study to test the applicability of imitating Tsallis entropy for performance enhancement: Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection. For the Bitcoin price prediction, models embedded with FINs reduced the root mean square error by around 1000 compared to the baseline. In the speech emotion recognition task, the FIN-augmented model increased classification accuracy by over 3 percent. Lastly, in the CNP detection experiment, an improvement of about 7 percent was observed compared to established classifiers. These findings validate the broad utility and potency of FINs in diverse applications.