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<!DOCTYPE html>
<!-- saved from url=(0045)./index.html -->
<html lang="en" style="" class=" js flexbox flexboxlegacy canvas canvastext webgl no-touch geolocation postmessage websqldatabase indexeddb hashchange history draganddrop websockets rgba hsla multiplebgs backgroundsize borderimage borderradius boxshadow textshadow opacity cssanimations csscolumns cssgradients cssreflections csstransforms csstransforms3d csstransitions fontface generatedcontent video audio localstorage sessionstorage webworkers applicationcache svg inlinesvg smil svgclippaths">
<head>
<title>machine learning</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<base target="_blank" />
<meta name="description" content="">
<meta name="keywords" content="">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<!-- Favicon -->
<link href="./images/favicon.ico" rel="shortcut icon">
<!-- Stylesheets -->
<link rel="stylesheet" href="./index_files/bootstrap.min.css">
<link rel="stylesheet" href="./index_files/font-awesome.min.css">
<link rel="stylesheet" href="./index_files/owl.carousel.css">
<link rel="stylesheet" href="./index_files/magnific-popup.css">
<link rel="stylesheet" href="./index_files/reset.css">
<link rel="stylesheet" href="./index_files/style.css">
<link rel="stylesheet" href="./index_files/style2.css">
<link rel="stylesheet" href="./index/bootstrap.css"> <!-- Bootstrap-Core-CSS -->
<link rel="stylesheet" href="./index/style.css" type="text/css" media="all" /> <!-- Style-CSS -->
<link rel="stylesheet" href="./index/swipebox.css">
<!-- ==== MODERNIZR ==== -->
<script src="./index_files/modernizr.js.下载"></script>
</head>
<body style="overflow: visible;">
<!-- ==== About Section Start ==== -->
<section class="about-section spad" id="interest">
<div class="container">
<h2>Machine Learning</h2>
<br>
<div class="col-md-12 col-sm-12">
<div class="about-text">
<br>
<br>
<ol>
<li>
<h3><a href="mlreview.pdf">An Introduction: Machine Learning vs Tensor Network</a></h3>
</li>
</ol>
<ul>
<li>
<p ></p></li>
</ul>
<br>
<ol>
<li>
<h3><a href="mlrelatedwork.html">Related Works In This Field</a></h3>
</li>
</ol>
<ul>
<li>
<p ></p></li>
</ul>
<br>
<ol>
<li>
<h3>Ours Works:</h3>
</li>
</ol>
<div class="container">
<ul>
<h3 ><a href="http://arxiv.org/abs/1701.04831">On the Equivalence of Restricted Boltzmann Machines and Tensor Network States</a></h3></ul>
<div class="container">
<ul>
<li>
<p >Restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and customer ratings, etc. We build a bridge between RBM and tensor network states (TNS) widely used in quantum many-body physics research. We devise efficient algorithms to translate an RBM into the commonly used TNS......</p></li>
</ul>
</div>
</div>
<br>
<div class="container">
<ul>
<h3 ><a href="http://arxiv.org/abs/1712.04144">Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines</a></h3></ul>
<div class="container">
<ul>
<li>
<p>We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data. The two approaches represent probabilities of observed data using energy-based models and quantum states respectively.Classical and quantum information patterns of the target datasets therefore provide principled guidelines for structural design and learning in these two approaches. Taking the restricted Boltzmann machines (RBM) as an example, we analyze the information theoretical bounds of the two approaches......</p>
</ul>
</div>
</div>
<br>
</div>
</div>
</div>
</section>
<!-- ==== About Section End ==== -->
</body></html>