Summary of Activations Through Extensions: a Framework to Boost Performance Of Neural Networks, by Chandramouli Kamanchi et al.
Activations Through Extensions: A Framework To Boost Performance Of Neural Networks
by Chandramouli Kamanchi, Sumanta Mukherjee, Kameshwaran Sampath, Pankaj Dayama, Arindam Jati, Vijay Ekambaram, Dzung Phan
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Numerical Analysis (math.NA)
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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 paper proposes a framework for unifying various works on activation functions in neural networks, which allows for theoretically explaining the performance benefits of these works. The authors introduce novel techniques that enable obtaining “extensions” (special generalizations) of neural networks through operations on activation functions. Experimental results demonstrate that these extensions can lead to improved performance with minimal added computational cost on standard test functions and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to connect the dots between what we put into a computer and what it outputs, using special math formulas called activation functions. These formulas help computers learn from things like pictures and sounds. The researchers found a way to make these formulas work together in a smart way, which makes computers do better on certain tasks. They also came up with new ideas that let them take existing computer programs and make them work even better without using too much extra processing power or memory. |
