Ken Kreutz-Delgado | DeepAI

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

We outline emerging opportunities and challenges to enhance the utility ...

26 Luca Pion-Tonachini, et al. ∙

∙ 10/15/2021

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

Quantization and pruning are core techniques used to reduce the inferenc...

0 Xinyu Zhang, et al. ∙

∙ 10/06/2021

Tuning Confidence Bound for Stochastic Bandits with Bandit Distance

We propose a novel modification of the standard upper confidence bound (...

0 Xinyu Zhang, et al. ∙

∙ 07/15/2021

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

A novel energy-efficient edge computing paradigm is proposed for real-ti...

0 Ian Colbert, et al. ∙

∙ 01/31/2021

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

The use of Deep Learning hardware algorithms for embedded applications i...

0 Siqiao Ruan, et al. ∙

∙ 01/30/2021

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

When trained as generative models, Deep Learning algorithms have shown e...

0 Ian Colbert, et al. ∙

∙ 10/28/2019

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

Stochastic-sampling-based Generative Neural Networks, such as Restricted...

0 Alexander Potapov, et al. ∙

∙ 03/11/2019

AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks

The power budget for embedded hardware implementations of Deep Learning ...

0 Ian Colbert, et al. ∙

∙ 01/22/2019

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

The electroencephalogram (EEG) provides a non-invasive, minimally restri...

0 Luca Pion-Tonachini, et al. ∙

∙ 05/07/2017

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

In recent years deep learning algorithms have shown extremely high perfo...

0 Xinyu Zhang, et al. ∙

∙ 04/13/2017

ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks

Probabilistic generative neural networks are useful for many application...

0 Xiaojing Xu, et al. ∙

∙ 03/26/2015

Gibbs Sampling with Low-Power Spiking Digital Neurons

Restricted Boltzmann Machines and Deep Belief Networks have been success...

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