Preliminary Map of Neural Network Weight Intensity Pattern Exploration recently contributed to an interdisciplinary publication, using machine learning techniques to separate climate ‘signals’ from internal noise across climate models. and isolate climate change patterns.Viewing forced climate patterns through an AI Lens, published in Geophysical Research Letters Interpretable analysis of neural networks is one of the […]
Pattern Exploration was pleased to present some AI research at this year, 2019’s Society for Imaging Informatics in Medicine, SIIM conference in Denver, CO. Our presentation, titled “A Two-Stage Deep Learning Approach to Chest X-Ray Analysis” Pattern Exploration Presentation Our research and presentation highlighted PE’s fundamental philosophy about small, interpretable neural networks that buck the […]
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Comparing Numpy, Pytorch, and autograd on CPU and GPU¶ by Chuck Anderson, Pattern Exploration This post is available for downloading as this jupyter notebook. Table of Contents¶ Very Brief Introduction to Autograd Using Numpy to Fit a Polynomial to Data Now, with Pytorch Pytorch with Autograd Pytorch with autograd on GPU Wrapped up in one […]
Numpy versus Pytorch¶ by Chuck Anderson, Pattern Exploration Here the accuracy and computation time of the training of simple fully-connected neural networks using numpy and pytorch implementations and applied to the MNIST data set are compared. The Adam optimization algorithm in numpy and pytorch are compared, as well as the Scaled Conjugate Gradient optimization algorithm […]
Fast Reinforcement Learning After Pretraining¶ by Chuck Anderson, Pattern Exploration We presented at IJCNN, 2015 the following paper, which won the Best Paper Award Anderson, C., Lee, M., and Elliott, D., “Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics“, Proceedings of the IJCNN, 2015, Killarney, Ireland. Abstract: Deep learning algorithms have recently […]
