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Magnetic Tunnel Junction (MTJ) as Stochastic Neurons and Synapses: Stochastic Binary Neural Networks, Bayesian Inferencing, Optimization Problems
26 Oct 2018 | | Contributor(s):: Abhronil Sengupta, Kaushik Roy
In this presentation, we provide a multi-disciplinary perspective across the stack of devices, circuits, and algorithms to illustrate how the stochastic switching dynamics of spintronic devices in the presence of thermal noise can provide a direct mapping to the units of such computing...
Re-Engineering Computing For Next Generation Autonomous Intelligent Systems: Devices, Circuits, and Algorithms
27 Aug 2018 | | Contributor(s):: Kaushik Roy, Abhronil Sengupta
Advances in machine learning, notably deep learning, have led to computers matching or surpassing human performance in several cognitive tasks including vision, speech and natural language processing. However, implementation of such neural algorithms in conventional "von-Neumann"...
Deep Machine Learning for Machine Performance and Damage Prediction
08 Aug 2018 | | Contributor(s):: Elijah Reber, Nickolas D Winovich, Guang Lin
Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at...
Is More Data Better Than Better Algorithms in Machine Learning?
08 Jun 2018 |
Posted by Cogito Tech LLC
Yes in machine learning more data is always better than better algorithms. Actually, the quality of data defines how the inputs will work in machine learning training and output would be exactly...
Ronald James Cortese
Gloria Wahyu Budiman