83 citations · 96 across the 11 of their papers we have counts for
3 papers · 1 filter
Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation
Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair
Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance. This paper studies their behavior for three algorithm…
Quantifying Inherent Randomness in Machine Learning Algorithms
Soham Raste, Rahul Singh, Joel Vaughan +1
Most machine learning (ML) algorithms have several stochastic elements, and their performances are affected by these sources of randomness. This paper uses an empirical study to sy…
Interpretable Feature Engineering for Time Series Predictors using Attention Networks
Tianjie Wang, Jie Chen, Joel Vaughan +1
Regression problems with time-series predictors are common in banking and many other areas of application. In this paper, we use multi-head attention networks to develop interpreta…