Summary of On the Role Of Activation Functions in Eeg-to-text Decoder, by Zenon Lamprou et al.
On the Role of Activation Functions in EEG-To-Text Decoder
by Zenon Lamprou, Iakovos Tenedios, Yashar Moshfeghi
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This research paper explores the application of neuroscience principles in information retrieval, specifically focusing on improving text generation using EEG data. The authors attempt to build upon an initial effort by optimizing neural network performance through various activation functions. Their results show that introducing a higher-degree polynomial activation function can enhance model performance without modifying the architecture. Additionally, they find that the learnable 3rd-degree activation function outperforms its non-learnable counterpart on 1-gram evaluations, but underperforms when applied to 2-grams and above. The leaky ReLU activation function, however, surpasses the baseline in these higher-order evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to use brain signals from EEG data to help computers understand and generate text better. To make this work, they need to improve how neural networks process information. This paper looks at different ways to make these networks work better by using special functions called activation functions. The results show that using a special kind of polynomial function can actually make the computer generate text more accurately. However, when looking at longer phrases, another type of function called leaky ReLU does even better. |
Keywords
» Artificial intelligence » Neural network » Relu » Text generation
