Itamar Arel

University of Tennessee at Knoxville — United States

Computer Science · Physical Sciences

15h-index3.0kcitations52works0.02yr avg
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Research Topics

Neural Networks and Applications(19), Advanced Memory and Neural Computing(7), Domain Adaptation and Few-Shot Learning(7), Time Series Analysis and Forecasting(6), Neural dynamics and brain function(5)

Publications52 total

Faster Gated Recurrent Units via Conditional Computation
2016· 3 cited
Sequential Covariance-Matrix Estimation with Application to Mitigating Catastrophic Forgetting
2015· 6 cited
Analog inference circuits for deep learning
2015· 6 cited
Neuron clustering for mitigating catastrophic forgetting in feedforward neural networks
2014· 7 cited
A 1 TOPS/W Analog Deep Machine-Learning Engine With Floating-Gate Storage in 0.13 µm CMOS
IEEE Journal of Solid-State Circuits·2014· 125 cited
Unsupervised neuron selection for mitigating catastrophic forgetting in neural networks
2014· 23 cited
30.10 A 1TOPS/W analog deep machine-learning engine with floating-gate storage in 0.13μm CMOS
2014· 3 cited
Success in Multidisciplinary Research (2014)
2014
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks
2013· 6 citedOpen Access
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks
arXiv (Cornell University)·2013· 49 citedOpen Access
Low-Rank Approximations for Conditional Feedforward Computation
arXiv (Cornell University)·2013Open Access
An analog online clustering circuit in 130nm CMOS
2013· 9 cited
On the Impact of Approximate Computation in an Analog DeSTIN Architecture
IEEE Transactions on Neural Networks and Learning Systems·2013· 13 cited
Mitigation of catastrophic forgetting in recurrent neural networks using a Fixed Expansion Layer
2013· 19 cited
Hierarchical spatiotemporal feature extraction using recurrent online clustering
Pattern Recognition Letters·2013· 12 cited
Ensemble Learning in Fixed Expansion Layer Networks for Mitigating Catastrophic Forgetting
IEEE Transactions on Neural Networks and Learning Systems·2013· 65 cited
Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models
arXiv (Cornell University)·2013Open Access
Recurrent Online Clustering as a Spatio-Temporal Feature Extractor in DeSTIN
arXiv (Cornell University)·2013· 1 citedOpen Access
Recurrent Clustering for Unsupervised Feature Extraction with Application to Sequence Detection
2012· 3 cited
Jensen-Shannon Divergence in Ensembles of Concurrently-Trained Neural Networks
2012· 2 cited
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Frequent Co-authors

Steven R. Young(10), Derek Rose(9), Thomas P. Karnowski(7), Robert Coop(5), Junjie Lu(5), Jeremy Holleman(5), Andrew S. Davis(5), Ben Goodrich(4), Ben Goertzel(3), Aaron Mishtal(3), Thomas Urbanik(2), Ruiting Lian(2), Hugo de Garis(2), Ken Habgood(2), Vincent Paquit(2), Christopher T. Symons(2), George Bitar(2), Andrew Davis(2), Chuqiao Liu(1), Airton G Kohls(1)