1 citations · 3 across the 4 of their papers we have counts for
4 papers
Regularization of ML models for Earth systems by using longer model timesteps
Raghul Parthipan, Mohit Anand, Hannah M Christensen +3
Regularization is a technique to improve generalization of machine learning (ML) models. A common form of regularization in the ML literature is to train on data where similar inpu…
Defining error accumulation in ML atmospheric simulators
Raghul Parthipan, Mohit Anand, Hannah M. Christensen +2
Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather. Many of these ML models are autoregressive, and error accumulati…
A Combined Convex Model for Travel Demand Forecasting with Hierarchical Extended Logit Model
Youngseo Kim, Samitha Samaranayake, Damon Wischik
The travel demand forecasting model plays a crucial role in evaluating large-scale infrastructure projects, such as the construction of new roads or transit lines. While combined m…
Taylorformer: Probabilistic Modelling for Random Processes including Time Series
Omer Nivron, Raghul Parthipan, Damon J. Wischik
We propose the Taylorformer for random processes such as time series. Its two key components are: 1) the LocalTaylor wrapper which adapts Taylor approximations (used in dynamical s…