با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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 |