3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.NE2025
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,…