Summary of Hysteresis Activation Function For Efficient Inference, by Moshe Kimhi et al.
Hysteresis Activation Function for Efficient Inference
by Moshe Kimhi, Idan Kashani, Avi Mendelson, Chaim Baskin
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 The proposed Hysteresis Rectified Linear Unit (HeLU) activation function aims to mitigate the “dying ReLU” problem in traditional ReLU-based networks. HeLU employs a variable threshold that refines backpropagation, allowing simpler activation functions to achieve competitive performance comparable to more complex counterparts without introducing unnecessary complexity or requiring inductive biases. This is achieved through a refined mechanism that adapts to the training process, enabling better model generalization across diverse datasets. The proposed method demonstrates promising results for efficient and effective inference suitable for various neural network architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReLU is a popular activation function due to its hardware efficiency, but it has issues like “dying ReLU,” where neurons fail to activate during training. To fix this, researchers propose HeLU, an activation function that uses a variable threshold to refine backpropagation. This helps simpler activation functions perform well without adding complexity or biases. The results show that HeLU improves model generalization and is suitable for various neural network architectures. |
Keywords
» Artificial intelligence » Backpropagation » Generalization » Inference » Neural network » Relu
