Search Results for author:
Found 3 papers, 1 papers with code
AutoDOViz: Human-Centered Automation for Decision Optimization
AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts.
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales
Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions.
TSML (Time Series Machine Learnng)
Over the past years, the industrial sector has seen many innovations brought about by automation.
