![]() ![]() Therefore it is really trivial to say "elu or relu is the best performing activation function" without specifying the task.What you should really do when you see a new activation function is to add it into your neural architecture search algorithm, so that it can determine whether this new activation function is useful for your task or not. relu or gelu) that will perform universally well on all tasks (free lunch). This is often referred to as "no free lunch" theorem in optimization, as there is not one activation function (eg. The best neural architecture and activation function really depends on the nature of your application. The ReLU is x * 1(x>0) and x * Phi(x) is a smoother version. I think both of these activations are generally better than the ReLU. Generally nonlinearities with learnable hyperparameters can beat those without hyperparameters, but there is an added risk of overfitting.Ĭurrently, I see people using x * sigmoid(x) in NAS papers and people using the GELU in NLP papers with Transformers/BERT. I should say the function space of x*sigmoid(a*x) and x*Phi(a*x) is approximately the same. I thank the swish authors for swiftly citing our work after this was brought to their attention, especially since I was an irrelevant undergraduate at the time Quoc was quite kind to me whenever I visited Brain this past summer, and I have since co-authored a paper with Barret, another swish author. Hence the swish is a nonlinearity with learnable hyperparameters. (2017), so the swish was modified to become swish(a,x) = x*sigmoid(a*x). Then the authors became aware that x * sigmoid(x) was quite similar to the GELU and x * sigmoid(x) was called the SiLU in the GELU paper (2016), and x * sigmoid(x) was also re-proposed in Elfwing et al. However, 1.5 years after we put up this paper, the swish paper re-proposed x*sigmoid(x). We had the choice between the SiLU and the GELU, and we chose the GELU. We noted x * sigmoid(x) generally does not do as well as the GELU(x) = x * Phi(x). In the GELU paper we introduced the SiLU(x) = x * sigmoid(x). In the GPT-2 paper, they use GELU activation in all the decoder blocks, so GELU is definitely being used in SOTA methods. Methods like GELU and Swish are attempting to provide 'some' well defined gradient in the negative regime to stop neurons dying while bounding how far into the negative regime activations are able to have an effect which allows for better mixing of features between layers. This forces additional activations to be needed such that constructive and destructive interference balances out. However, Leaky-RELU being scaled-linear in the negative regime means that strong negative activations can have an undesirable impact on the sum of activations feeding into the the unit of the next layer. Leaky-RELU prevents activated units in the negative regime from having a zero-gradient which can prevent neurons from 'dying off' which can occur until other weights change and cause the activation to become positive again. The whole point of all of these RELU-like activation functions is preserving linearity in the positive activations and suppressing the negative activations. Swish consistently performs slightly better then GELU across a range of experiments, and in some implementations is more efficient. Metacademy is a great resource which compiles lesson plans on popular machine learning topics.įor Beginner questions please try /r/LearnMachineLearning, /r/MLQuestions or įor career related questions, visit /r/cscareerquestions/ Please have a look at our FAQ and Link-Collection Rules For Posts + Research + Discussion + Project + News on Twitter Chat with us on Slack Beginners: ![]()
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