Essays about: "Matrix complexity"
Showing result 21 - 25 of 94 essays containing the words Matrix complexity.
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21. Using Artificial Neural Networks to optimize scattering probabilities
University essay from Lunds universitet/Teoretisk partikelfysik - Geonomgår omorganisationAbstract : Monte Carlo event generators are used by theoretical particle physicists to get a better understanding of the phenomena in particle physics. Given the improvements in precision and accuracy of event generators, using these tools can be very CPU intensive. READ MORE
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22. Development of Neural Networks Using Deterministic Transforms
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep neural networks have been a leading research topic within the machine learning field for the past few years. The introduction of graphical processing units (GPUs) and hardware advances made possible the training of deep neural networks. Previously the training procedure was impossible due to the huge amount of training samples required. READ MORE
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23. The Use of Big Data in Process Management : A Literature Study and Survey Investigation
University essay from Linköpings universitet/Logistik- och kvalitetsutvecklingAbstract : In recent years there has been an increasing interest in understanding how organizations can utilize big data in their process management to create value and improve their processes. This is due to new challenges for process management which have arisen from increasing competition and the complexity of large data sets due to technological advancements. READ MORE
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24. Random projections in a distributed environment for privacy-preserved deep learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The field of Deep Learning (DL) only over the last decade has proven useful for increasingly more complex Machine Learning tasks and data, a notable milestone being generative models achieving facial synthesis indistinguishable from real faces. With the increased complexity in DL architecture and training data, follows a steep increase in time and hardware resources required for the training task. READ MORE
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25. Machine Unlearning and hyperparameters optimization in Gaussian Process regression
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The establishment of the General Data Protection Regulation (GDPR) in Europe in 2018, including the "Right to be Forgotten" poses important questions about the necessity of efficient data deletion techniques for trained Machine Learning models to completely enforce this right, since retraining from scratch such models whenever a data point must be deleted seems impractical. We tackle such a problem for Gaussian Process Regression and define in this paper an efficient exact unlearning technique for Gaussian Process Regression which completely include the optimization of the hyperparameters of the kernel function. READ MORE
