2 citations · 5 across the 9 of their papers we have counts for
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SMU: smooth activation function for deep networks using smoothing maximum technique
Koushik Biswas, Sandeep Kumar, Shilpak Banerjee +1
Deep learning researchers have a keen interest in proposing two new novel activation functions which can boost network performance. A good choice of activation function can have si…
SAU: Smooth activation function using convolution with approximate identities
Koushik Biswas, Sandeep Kumar, Shilpak Banerjee +1
Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin. Over the years, many smooth approximations of ReLU have been proposed using various sm…
ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions
Koushik Biswas, Sandeep Kumar, Shilpak Banerjee +1
An activation function is a crucial component of a neural network that introduces non-linearity in the network. The state-of-the-art performance of a neural network depends also on…
Tropical cyclone intensity estimations over the Indian ocean using Machine Learning
Koushik Biswas, Sandeep Kumar, Ashish Kumar Pandey
Tropical cyclones are one of the most powerful and destructive natural phenomena on earth. Tropical storms and heavy rains can cause floods, which lead to human lives and economic…
Intensity Prediction of Tropical Cyclones using Long Short-Term Memory Network
Koushik Biswas, Sandeep Kumar, Ashish Kumar Pandey
Tropical cyclones can be of varied intensity and cause a huge loss of lives and property if the intensity is high enough. Therefore, the prediction of the intensity of tropical cyc…
Orthogonal-Padé Activation Functions: Trainable Activation functions for smooth and faster convergence in deep networks
Koushik Biswas, Shilpak Banerjee, Ashish Kumar Pandey
We have proposed orthogonal-Padé activation functions, which are trainable activation functions and show that they have faster learning capability and improves the accuracy in stan…