University of South Florida researchers propose TeLU activation function for fast and stable deep learning

Inspired by the brain, neural networks They are essential for recognizing images and processing language. These networks depend on activation functions that allow them to learn complex patterns. However, many activation functions face challenges. Some struggle with vanishing gradientswhich slows down learning in deep networks, while others suffer”dead neurons”, where certain parts of the network stop learning. Modern alternatives aim to solve these problems, but often suffer from drawbacks such as inefficiency or inconsistent performance.

At the moment, activation functions in neural networks face important problems. Features like passed and sigmoid struggle with vanishing gradients, which limits their effectiveness in deep networks, and while suspicious improved this slightly, which turned out to have other problems. resume addresses some gradient problems but introduces the “dying resume”problem, inactivating the neurons. Variants like Leaky ReLU and PRELU They try to solve them, but they bring with them inconsistencies and challenges in regularization. Advanced features like ABOVE, SiLUand shave improve nonlinearities. However, it adds complexity and biases, while newer designs like Mish and Smish showed stability only in specific cases and did not work in general cases.

To solve these problems, researchers from University of South Florida proposed a new activation function, TeLU(x) = x · tanh(ex)that combines the learning efficiency of resume with the stability and generalization capabilities of fluid functions. This function introduces smooth transitions, meaning that the output of the function changes gradually as the input changes, near-zero mean activations, and robust gradient dynamics to overcome some of the problems of existing activation functions. The design aims to provide consistent performance across various tasks, improve convergence, and improve stability with better generalization on shallow and deep architectures.

The researchers focused on improving neural networks while maintaining computational efficiency. The researchers set out to converge the algorithm quickly, keep it stable during training, and make it robust to generalization to unseen data. The function exists in a non-polynomial and analytical way; therefore, it can approximate any continuous objective function. The approach emphasized improving learning stability and self-regulation while minimizing numerical instability. By combining linear and nonlinear properties, the framework can support efficient learning and help avoid problems such as gradient explosion.

Researchers evaluated TeLU's performance through experiments and compared it with other activation functions. The results showed that TeLU It helped prevent the vanishing gradient problem, which is important for effectively training deep networks. It was tested on large data sets like ImagenNet and Dynamic Grouping Transformers in Text8showing faster convergence and higher accuracy than traditional functions such as resume. The experiments also showed that TeLU It is computationally efficient and works well with ReLU-based setups, often leading to better results. The experiments confirmed that TeLU It is stable and works best on various neural network architectures and training methods.

In the end, the activation function proposed by the researchers solved the key challenges of existing activation functions by preventing the vanishing gradient problem, improving computational efficiency, and showing better performance on various data sets and architectures. Its successful application on benchmarks such as ImageNet, Text8, and Penn Treebank, showing faster convergence, accuracy improvements, and stability in deep learning models, may position TeLU as a promising tool for deep neural networks. Furthermore, the performance of TeLU can serve as a foundation for future research, which can inspire further development of activation functions to achieve even greater efficiency and reliability in machine learning advancements.

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Divyesh is a Consulting Intern at Marktechpost. He is pursuing a BTech in Agricultural and Food Engineering from the Indian Institute of technology Kharagpur. He is a data science and machine learning enthusiast who wants to integrate these leading technologies in agriculture and solve challenges.