paper

docs/index.html

@@ -133,6 +133,9 @@ <h1 id="welcome-to-auto-keras">Welcome to Auto-Keras</h1>
 It aims at automatically search for the architecture and hyperparameters for deep learning models.
 The ultimate goal for this project is for domain experts in fields other than computer science or machine learning
 to use deep learning models conveniently.</p>
+<p>To install the package please use the commend as follows:</p>
+<pre><code>pip install autokeras
+</code></pre>
 <p>Here is a short example for using the package.</p>
 <pre><code>import autokeras as ak
 
@@ -142,6 +145,21 @@ <h1 id="welcome-to-auto-keras">Welcome to Auto-Keras</h1>
 results = clf.predict(x_test)
 </code></pre>
 <p>For the repository on GitHub visit <a href="https://github.com/jhfjhfj1/autokeras">Auto-Keras on GitHub</a>.</p>
+<p>If you use Auto-Keras in a scientific publication, we would appreciate references to the following paper:</p>
+<p>Efficient Neural Architecture Search with Network Morphism.
+Haifeng Jin, Qingquan Song, Xia Hu.
+<a href="https://arxiv.org/abs/1806.10282">arXiv:1806.10282</a>.</p>
+<p>Biblatex entry:</p>
+<pre><code>@online{jin2018efficient,
+  author       = {Haifeng Jin and Qingquan Song and Xia Hu},
+  title        = {Efficient Neural Architecture Search with Network Morphism},
+  date         = {2018-06-27},
+  year         = {2018},
+  eprintclass  = {cs.LG},
+  eprinttype   = {arXiv},
+  eprint       = {cs.LG/1806.10282},
+}
+</code></pre>
 
             </div>
           </div>
@@ -191,5 +209,5 @@ <h1 id="welcome-to-auto-keras">Welcome to Auto-Keras</h1>
 
 <!--
 MkDocs version : 0.17.4
-Build Date UTC : 2018-07-02 01:31:09
+Build Date UTC : 2018-07-02 02:25:40
 -->

docs/search/search_index.json

@@ -2,12 +2,12 @@
     "docs": [
         {
             "location": "/",
-            "text": "Welcome to Auto-Keras\n\n\n\n\n\n\nThis is a automated machine learning (AutoML) package based on Keras. \nIt aims at automatically search for the architecture and hyperparameters for deep learning models.\nThe ultimate goal for this project is for domain experts in fields other than computer science or machine learning\nto use deep learning models conveniently.\n\n\nHere is a short example for using the package.\n\n\nimport autokeras as ak\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nclf = ak.ImageClassifier()\nclf.fit(x_train, y_train)\nresults = clf.predict(x_test)\n\n\n\nFor the repository on GitHub visit \nAuto-Keras on GitHub\n.",
+            "text": "Welcome to Auto-Keras\n\n\n\n\n\n\nThis is a automated machine learning (AutoML) package based on Keras. \nIt aims at automatically search for the architecture and hyperparameters for deep learning models.\nThe ultimate goal for this project is for domain experts in fields other than computer science or machine learning\nto use deep learning models conveniently.\n\n\nTo install the package please use the commend as follows:\n\n\npip install autokeras\n\n\n\nHere is a short example for using the package.\n\n\nimport autokeras as ak\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nclf = ak.ImageClassifier()\nclf.fit(x_train, y_train)\nresults = clf.predict(x_test)\n\n\n\nFor the repository on GitHub visit \nAuto-Keras on GitHub\n.\n\n\nIf you use Auto-Keras in a scientific publication, we would appreciate references to the following paper:\n\n\nEfficient Neural Architecture Search with Network Morphism.\nHaifeng Jin, Qingquan Song, Xia Hu.\n\narXiv:1806.10282\n.\n\n\nBiblatex entry:\n\n\n@online{jin2018efficient,\n  author       = {Haifeng Jin and Qingquan Song and Xia Hu},\n  title        = {Efficient Neural Architecture Search with Network Morphism},\n  date         = {2018-06-27},\n  year         = {2018},\n  eprintclass  = {cs.LG},\n  eprinttype   = {arXiv},\n  eprint       = {cs.LG/1806.10282},\n}",
             "title": "Home"
         },
         {
             "location": "/#welcome-to-auto-keras",
-            "text": "This is a automated machine learning (AutoML) package based on Keras. \nIt aims at automatically search for the architecture and hyperparameters for deep learning models.\nThe ultimate goal for this project is for domain experts in fields other than computer science or machine learning\nto use deep learning models conveniently.  Here is a short example for using the package.  import autokeras as ak\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nclf = ak.ImageClassifier()\nclf.fit(x_train, y_train)\nresults = clf.predict(x_test)  For the repository on GitHub visit  Auto-Keras on GitHub .",
+            "text": "This is a automated machine learning (AutoML) package based on Keras. \nIt aims at automatically search for the architecture and hyperparameters for deep learning models.\nThe ultimate goal for this project is for domain experts in fields other than computer science or machine learning\nto use deep learning models conveniently.  To install the package please use the commend as follows:  pip install autokeras  Here is a short example for using the package.  import autokeras as ak\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nclf = ak.ImageClassifier()\nclf.fit(x_train, y_train)\nresults = clf.predict(x_test)  For the repository on GitHub visit  Auto-Keras on GitHub .  If you use Auto-Keras in a scientific publication, we would appreciate references to the following paper:  Efficient Neural Architecture Search with Network Morphism.\nHaifeng Jin, Qingquan Song, Xia Hu. arXiv:1806.10282 .  Biblatex entry:  @online{jin2018efficient,\n  author       = {Haifeng Jin and Qingquan Song and Xia Hu},\n  title        = {Efficient Neural Architecture Search with Network Morphism},\n  date         = {2018-06-27},\n  year         = {2018},\n  eprintclass  = {cs.LG},\n  eprinttype   = {arXiv},\n  eprint       = {cs.LG/1806.10282},\n}",
             "title": "Welcome to Auto-Keras"
         },
         {

mkdocs/docs/index.md

@@ -8,6 +8,10 @@ It aims at automatically search for the architecture and hyperparameters for dee
 The ultimate goal for this project is for domain experts in fields other than computer science or machine learning
 to use deep learning models conveniently.
 
+To install the package please use the commend as follows:
+
+    pip install autokeras
+
 Here is a short example for using the package.
 
 
@@ -20,3 +24,20 @@ Here is a short example for using the package.
 
 For the repository on GitHub visit [Auto-Keras on GitHub](https://github.com/jhfjhfj1/autokeras).
 
+If you use Auto-Keras in a scientific publication, we would appreciate references to the following paper:
+
+Efficient Neural Architecture Search with Network Morphism.
+Haifeng Jin, Qingquan Song, Xia Hu.
+[arXiv:1806.10282](https://arxiv.org/abs/1806.10282).
+
+Biblatex entry:
+
+    @online{jin2018efficient,
+      author       = {Haifeng Jin and Qingquan Song and Xia Hu},
+      title        = {Efficient Neural Architecture Search with Network Morphism},
+      date         = {2018-06-27},
+      year         = {2018},
+      eprintclass  = {cs.LG},
+      eprinttype   = {arXiv},
+      eprint       = {cs.LG/1806.10282},
+    }