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نتیجه جستجو - Probabilistic machine learning

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
آموزش طبقه‌بندی‌کننده‌های ترکیبی کلاسیک-کوانتومی از طریق بهینه‌سازی تغییرات تصادفی-2022
Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this work, we study a two-layer hybrid classical-quantum classifier in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second classical combining layer. The input to the first, hidden, layer is obtained via amplitude encoding in order to leverage the exponential size of the fan-in of the quantum neurons in the number of qubits per neuron. To facilitate implementation of the QGLMs, all weights and activations are binary. While the state of the art on training strategies for this class of models is limited to exhaustive search and single-neuron perceptron-like bit-flip strategies, this letter introduces a stochastic variational optimization approach that enables the joint training of quantum and classical layers via stochastic gradient descent. Experiments show the advantages of the approach for a variety of activation functions implemented by QGLM neurons.
Index Terms: Probabilistic machine learning | quantum computing | quantum machine learning.
مقاله انگلیسی
2 Parameters estimation in Ebola virus transmission dynamics model based on machine learning
برآورد پارامترها در مدل دینامیک انتقال ویروس ابولا بر اساس یادگیری ماشین-2019
This paper presents the application of machine learning to parameter estimation in biomathematical model. The background of Ebola disease was introduced, including the structure and morphology of the virus, the causes of disease, the mode of transmission, prevention and control measures. Meanwhile, it is essential to present the mechanism of this method, the application and calculation process, and the parameters. Compared with other methods, this method can not only obtain more accurate parameter values based on fewer and scattered data, but also estimate the parameters appearing anywhere in the partial differential equation, and automatically filter arbitrary noise data through Gaussian priori hypothesis.
Keywords: Ebola | Probabilistic machine learning | Multi-output Gaussian process | Kernel function
مقاله انگلیسی
3 Prototyping a GPGPU Neural Network for Deep-Learning Big Data Analysis
نمونه سازی شبکه عصبی GPGPU برای یادگیری عمیق تجزیه و تحلیل داده های بزرگ-2017
Big Data concerns with large-volume complex growing data. Given the fast development of data storage and network, organizations are collecting large ever-growing datasets that can have useful information. In order to extract information from these datasets within useful time, it is important to use distributed and parallel algorithms. One common usage of big data is machine learning, in which collected data is used to predict future behavior. Deep-Learning using Artificial Neural Networks is one of the popular methods for extracting information from complex datasets. Deep-learning is capable of more creating complex models than traditional probabilistic machine learning techniques. This work presents a step-by-step guide on how to prototype a Deep-Learning application that executes both on GPU and CPU clusters. Python and Redis are the core supporting tools of this guide. This tutorial will allow the reader to understand the basics of building a distributed high performance GPU application in a few hours. Since we do not depend on any deep-learning application or framework—we use low-level building blocks—this tutorial can be adjusted for any other parallel algorithm the reader might want to prototype on Big Data. Finally, we will discuss how to move from a prototype to a fully blown production application.
Keywords:Big-dat|Deep-learning|Prototyping|GPGPU|Cluster|Distributed|Parallel programming
مقاله انگلیسی
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