384 citations · 650 across the 17 of their papers we have counts for
4 papers · 1 filter
Learning from learning machines: a new generation of AI technology to meet the needs of science
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin +33
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science cre…
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
Xinyu Zhang, Ian Colbert, Ken Kreutz-Delgado +1
Quantization and pruning are core techniques used to reduce the inference costs of deep neural networks. State-of-the-art quantization techniques are currently applied to both the…
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
Siqiao Ruan, Ian Colbert, Ken Kreutz-Delgado +1
The use of Deep Learning hardware algorithms for embedded applications is characterized by challenges such as constraints on device power consumption, availability of labeled data,…
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado +2
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, im…