Navigating Neural Networks: Exploring State-of-the-Art Activation Functions – OMSCS 7641: Machine Learning
Navigating Neural Networks: Exploring State-of-the-Art Activation Functions – OMSCS 7641: Machine Learning
Summary
Activation functions are crucial in neural networks, introducing non-linearity and enabling the modeling of complex patterns across varied tasks. This guide delves into the evolution, characteristics, and applications of state-of-the-art activation functions, illustrating their role in enhancing neural network performance. It discusses the transition from classic functions like sigmoid and tanh to advanced ones such as ReLU and its variants, addressing challenges like the vanishing gradient problem and the dying ReLU issue. Concluding with practical heuristics for selecting activation functions, the article emphasizes the importance of considering network architecture and task specifics, highlighting the rich diversity of activation functions available for optimizing neural network designs.
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Note: For this guide, we’ll focus on functions that operate on the scalar preactivations at each neuron individually.
Very frustrating to see this, as large models have shown that scalar activation functions make only a tiny impact when your model is wide enough.
https://arxiv.org/abs/2002.05202v1 shows GLU-based activation functions (2 inputs->1 output) almost universally beat their equivalent scalar functions. IMO there needs to be more work around these kinds of multi-input constructions, as there are much bigger potential gains.
E.g. even for cases where the network only needs static routing (tabular data), transformers sometimes perform magically better than MLPs. This suggests there's something special about self-attention as an "activation function". If that magic can be extracted and made sub-quadratic, it could be a paradigm shift in NN design.
The authors of the blog post seem aware of the limitations of their focus:
In contrast, ReLU and its variants are often preferred for the hidden layers on large datasets and deeper models as they accelerate training. CNNs frequently benefit from the ReLU variants and the Swish activation function. When training a DNN, Leaky ReLU is generally a good starting point. Alternatively, one can chose ReLU activations and inspect the percentage of dead neurons, switching to LeakyReLU or PReLU if required. GeLU shines in NLP tasks despite its computational cost. Swish, while promising, is relatively new and requires further exploration, interpretability and testing.
The activation function landscape is rich and diverse, offering a spectrum of choices to cater to various neural network needs. I hope this guide served as a good starting point for more exploration based on your requirements and network design.
Thank you for highlighting this research! At first glance it's interesting that sigmoid functions re-emerge as more useful using the approaches evaluated in that article.