Summary of Adaptive Activation Functions For Predictive Modeling with Sparse Experimental Data, by Farhad Pourkamali-anaraki et al.
Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data
by Farhad Pourkamali-Anaraki, Tahamina Nasrin, Robert E. Jensen, Amy M. Peterson, Christopher J. Hansen
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML)
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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 investigates the impact of adaptive activation functions on classification accuracy and predictive uncertainty in limited-data settings. Adaptive activation functions are crucial for introducing non-linear structures that capture intricate input-output patterns, but their effectiveness has been studied mainly in domains with ample data. This study examines two types of adaptive activation functions – Exponential Linear Unit (ELU) and Softplus – with shared and individual trainable parameters per hidden layer. The functions are tested on three testbeds derived from additive manufacturing problems containing fewer than one hundred training instances. The results show that adaptive activation functions with individual trainable parameters result in accurate and confident prediction models, outperforming fixed-shape activation functions and identical trainable activation functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make neural networks better by changing the way they “wake up” hidden layers. Right now, some networks are really good at one thing, but struggle with others. This is because of the way they learn. The researchers want to see if making these “wakes ups” adjustable will help. They tested two special ways of doing this on problems that need fewer than 100 examples to learn from. What they found was that these new methods work really well and are better at predicting things than other ways of doing things. This is important because it can help us make better machines that can solve real-world problems. |
Keywords
* Artificial intelligence * Classification
