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Ken Kreutz-Delgado
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Learning from learning machines: a new generation of AI technology to meet the needs of science


Nov 27, 2021
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown

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Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations


Nov 01, 2021
Xinyu Zhang, Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

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Tuning Confidence Bound for Stochastic Bandits with Bandit Distance


Oct 06, 2021
Xinyu Zhang, Srinjoy Das, Ken Kreutz-Delgado

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An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling


Jul 26, 2021
Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

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Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework


Jan 31, 2021
Siqiao Ruan, Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

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A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?


Jan 30, 2021
Ian Colbert, Jake Daly, Ken Kreutz-Delgado, Srinjoy Das

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PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems


Oct 28, 2019
Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado, Alexander Cloninger, Srinjoy Das

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* Will be presented at the Asilomar Conference on Signals, Systems, and Computers 
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AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks


Mar 26, 2019
Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das

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