Summary of Enhancing Sequential Model Performance with Squared Sigmoid Tanh (sst) Activation Under Data Constraints, by Barathi Subramanian et al.
Enhancing Sequential Model Performance with Squared Sigmoid TanH (SST) Activation Under Data Constraints
by Barathi Subramanian, Rathinaraja Jeyaraj, Rakhmonov Akhrorjon Akhmadjon Ugli, Jeonghong Kim
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed squared Sigmoid TanH (SST) activation function aims to improve the learning capability of sequential neural network models under data constraints. The traditional Sigmoid and TanH activation functions used in recurrent neural networks, long short-term memory (LSTMs), and gated recurrent units (GRUs) can struggle to model sparse patterns when trained on small datasets. SST applies mathematical squaring to amplify differences between strong and weak activations, facilitating improved gradient flow and information filtering. This innovation is evaluated through experiments with LSTMs and GRUs for diverse applications like sign language recognition, regression, and time-series classification tasks, where the dataset is limited. Results show that SST models consistently outperform RNN-based models with baseline activations, achieving better test accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sequential neural networks can have trouble learning complex patterns when trained on small datasets. To fix this, researchers suggest using a new activation function called squared Sigmoid TanH (SST). This helps the model learn more effectively by making it easier to distinguish between important and unimportant information. The scientists tested SST with different types of models and tasks, such as recognizing sign language or predicting future values in a time series. They found that SST helped the models perform better than usual. |
Keywords
* Artificial intelligence * Classification * Neural network * Regression * Rnn * Sigmoid * Tanh * Time series
