9 citations · 9 across the 3 of their papers we have counts for
4 papers
Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal +2
Most real-world datasets used for training supervised learning models are contaminated with noisy data and outliers leading to large prediction errors. This paper proposes a new ap…
Efficient Vectorized Backpropagation Algorithms for Training Feedforward Networks Composed of Quadratic Neurons
Mathew Mithra Noel, Venkataraman Muthiah-Nakarajan, Yug D Oswal
Higher order artificial neurons whose outputs are computed by applying an activation function to a higher order multinomial function of the inputs have been considered in the past,…
Alternate Loss Functions for Classification and Robust Regression Can Improve the Accuracy of Artificial Neural Networks
Mathew Mithra Noel, Arindam Banerjee, Yug Oswal +2
All machine learning algorithms use a loss, cost, utility or reward function to encode the learning objective and oversee the learning process. This function that supervises learni…
Biologically Inspired Oscillating Activation Functions Can Bridge the Performance Gap between Biological and Artificial Neurons
Matthew Mithra Noel, Shubham Bharadwaj, Venkataraman Muthiah-Nakarajan +2
The recent discovery of special human neocortical pyramidal neurons that can individually learn the XOR function highlights the significant performance gap between biological and a…