This is the napari tutorial for DL at MBL 2024
Aug 27, 2024
This study develops and evaluates a machine learning-based forecasting system for ozone and PM2.5 at multiple AQS sites in the Pacific Northwest, demonstrating improved accuracy and reliability over existing CTM-based forecasts.
Feb 24, 2023
This study developed machine learning models to improve O3 forecasts for Kennewick, WA, demonstrating improved accuracy and reduced computational resources compared to traditional chemical transport models.
Feb 10, 2022
This paper proposes a method of deep artificial neuromodulation for stochastic gradient descent, applying biological neuromodulation concepts to modify learning parameters at each layer in a deep neural network during training. The method shows adaptability to different models and new problems, demonstrating dynamic, location-specific learning strategies.
Dec 11, 2018
Overview of COMP 135 Introduction to Machine Learning and Data Mining at Tufts University, Spring 2016.
Jan 1, 2016