Summary of Deriving Activation Functions Using Integration, by Allen Hao Huang et al.
Deriving Activation Functions Using Integration
by Allen Hao Huang, Imanol Schlag
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 A novel approach to designing activation functions is proposed by focusing on their gradients and deriving the corresponding activation functions using integration. The Expanded Integral of the Exponential Linear Unit (xIELU) is introduced, a trainable piecewise activation function derived by integrating trainable affine transformations applied to the Exponential Linear Unit (ELU). xIELU combines two key properties for the gradient: a trainable and linearly increasing gradient for positive inputs, similar to Squared ReLU (ReLU^2), and a trainable gradient that can take negative values for negative inputs, inspired by Expanded SiLU (xSiLU). The trainable parameters in xIELU allow it to adaptively reduce its nonlinearity for higher-level representations deeper in the network. Experiments with Llama models trained on FineWeb Edu dataset show that xIELU achieves lower perplexity compared to popular activation functions like ReLU^2 and SwiGLU when matched for the same compute cost and parameter count. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has come up with a new way to design special math functions called activation functions. They wanted to make these functions better by focusing on how they change when input values change. This led them to create a new function called xIELU, which can be used in artificial intelligence models like language translation machines. The big idea behind xIELU is that it can adjust its behavior depending on the kind of data it’s processing. When tested with large AI models, xIELU did better than some other popular activation functions. |
Keywords
» Artificial intelligence » Llama » Perplexity » Relu » Translation
