2 citations · 2 across the 4 of their papers we have counts for
5 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…
Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors
Arian Khorasani, Nathaniel Chen, Yug D Oswal +3
How close are neural networks to the best they could possibly do? Standard benchmarks cannot answer this because they lack access to the true posterior p(y|x). We use class-conditi…
A Significantly Better Class of Activation Functions Than ReLU Like Activation Functions
Mathew Mithra Noel, Yug Oswal
This paper introduces a significantly better class of activation functions than the almost universally used ReLU like and Sigmoidal class of activation functions. Two new activatio…
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…