Essays about: "stochastic spiking neural networks"
Found 4 essays containing the words stochastic spiking neural networks.
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1. A Bayesian Bee Colony Algorithm for Hyperparameter Tuning of Stochastic SNNs : A design, development, and proposal of a stochastic spiking neural network and associated tuner
University essay from Uppsala universitet/Signaler och systemAbstract : With the world experiencing a rapid increase in the number of cloud devices, continuing to ensure high-quality connections requires a reimagining of cloud. One proponent, edge computing, consists of many distributed and close-to-consumer edge servers that are hired by the service providers. READ MORE
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2. Introducing GA-SSNN: A Method for Optimizing Stochastic Spiking Neural Networks : Scaling the Edge User Allocation Constraint Satisfaction Problem with Enhanced Energy and Time Efficiency
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : As progress within Von Neumann-based computer architecture is being limited by the physical limits of transistor size, neuromorphic comuting has emerged as a promising area of research. Neuromorphic hardware tends to be substantially more power efficient by imitating the aspects of computations in networks of neurons in the brain. READ MORE
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3. Stochastic spiking neural networks based on ferroelectric memory devices
University essay from Lunds universitet/Fasta tillståndets fysik; Lunds universitet/Fysiska institutionenAbstract : In the last decade the world has seen a massive explosion of activity in the ma chine learning field. While the benefits of machine learning are ever growing and more impressive, the drawbacks of the technology are becoming clear and can hardly be ignored. READ MORE
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4. Developing a spiking neural model of Long Short-Term Memory architectures
University essay from Lunds universitet/Fysiska institutionen; Lunds universitet/FörbränningsfysikAbstract : Current advances in Deep Learning have shown significant improvements in common Machine Learning applications such as image, speech and text recognition. Specifically, in order to process time series, deep Neural Networks (NNs) with Long Short-Term Memory (LSTM) units are widely used in sequence recognition problems to store recent information and to use it for future predictions. READ MORE
