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ردیف | عنوان | نوع |
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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 |