-
Notifications
You must be signed in to change notification settings - Fork 4
Expand file tree
/
Copy pathtable_try.html
More file actions
291 lines (277 loc) · 13.6 KB
/
table_try.html
File metadata and controls
291 lines (277 loc) · 13.6 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
<!DOCTYPE HTML>
<!--
Spectral by HTML5 UP
html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>Deep Learning @ UGA - Schedule</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<!--[if lte IE 8]><script src="assets/js/ie/html5shiv.js"></script><![endif]-->
<link rel="stylesheet" href="assets/css/main.css" />
<!--[if lte IE 8]><link rel="stylesheet" href="assets/css/ie8.css" /><![endif]-->
<!--[if lte IE 9]><link rel="stylesheet" href="assets/css/ie9.css" /><![endif]-->
</head>
<body>
<!-- Page Wrapper -->
<div id="page-wrapper">
<!-- Header -->
<header id="header">
<h1><a href="index.html">Deep Learning @ UGA</a></h1>
<nav id="nav">
<ul>
<li class="special">
<a href="#menu" class="menuToggle"><span>Menu</span></a>
<div id="menu">
<ul>
<li><a href="index.html">Home</a></li>
<li><a href="schedule.html">Schedule</a></li>
<li><a href="resources.html">Resources</a></li>
<li><a href="recordings.html">Recordings</a></li>
<li><a href="past_speakers.html">Past Speakers</a></li>
<li><a href="about_us.html">About Us</a></li>
<li><a href="alumni.html">Alumni</a></li>
</ul>
</div>
</li>
</ul>
</nav>
</header>
<!-- Main -->
<article id="main">
<header>
<h2>Resources</h2>
<p>Learning materials from workshop.</p>
</header>
<section class="wrapper style5">
<div class="inner">
<h3>Reading and code materials from workshop.</h3>
<table class="js-sort-table" id="demo1">
<thead>
<tr>
<th class="js-sort-number">Number</th>
<th>Material</th>
<th class="js-sort-string">Difficulty Rate</th>
<th>Related Workshop</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td><a href="https://arxiv.org/pdf/1606.05908.pdf">A tutorial resource for VAEs emphasizing the mathematical foundation</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>2</td>
<td><a href="https://github.com/sksq96/pytorch-summary/tree/master/torchsummary">A python package for summarizing your models similar to the Keras .summary() method</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>3</td>
<td><a href="https://arxiv.org/pdf/1705.07120.pdf">VAE with a modified mixture of variationals prior (VampPrior)</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>4</td>
<td><a href="https://github.com/jmtomczak/vae_vampprior">VAE with a modified mixture of variationals prior (VampPrior) github page</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>5</td>
<td><a href="https://openreview.net/pdf?id=Sy2fzU9gl">Beta-VAE paper. Also discussing the notions of disentanglement.</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>6</td>
<td><a href="https://arxiv.org/pdf/1706.02262.pdf">InfoVAE paper covering maximization of mutual information between inputs and intermediate codes a la InfoGAN techniques.</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>7</td>
<td><a href="https://arxiv.org/pdf/1701.03077.pdf">"A General and Adaptive Robust Loss Function."</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>8</td>
<td><a href="http://colah.github.io/posts/2015-08-Backprop/">A very good explanation of computational graphs.</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>9</td>
<td><a href="https://github.com/rszeto/moving-symbols">Github repository for generating moving digits. We will use these scripts to generate train/test data for our video based generative models. We will be able to control key independent generative features and hence be able to see if our model *truly* captures critical features.</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>10</td>
<td><a href="https://github.com/pytorch/examples/blob/master/vae/main.py">Basic VAE example in PyTorch</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>11</td>
<td><a href="https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0">Basic intro to Transpose Conv</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>12</td>
<td><a href="https://towardsdatascience.com/transpose-convolution-77818e55a123">Basic intro to Transpose Conv</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>13</td>
<td><a href="http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html">Basic intro to Conv arithmatic of all forms</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>14</td>
<td><a href="https://arxiv.org/pdf/1710.04019.pdf">Introduction to data topology</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>15</td>
<td><a href="http://colah.github.io/posts/2015-08-Understanding-LSTMs/">LSTM tutorial</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>16</td>
<td><a href="https://arxiv.org/pdf/0711.0189.pdf">Spectral clustering tutorial</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>17</td>
<td><a href="https://papers.nips.cc/paper/2183-half-lives-of-eigenflows-for-spectral-clustering.pdf">Eigen-cuts algorithm</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>18</td>
<td><a href="https://github.com/ncullen93/torchsample/blob/master/README.md">Utility package we ought to implement</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>19</td>
<td><a href="https://arxiv.org/pdf/1706.06982.pdf">Two-stream dynamic texture synthesis</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>20</td>
<td><a href="https://arxiv.org/pdf/1901.11390.pdf">MONet: Unsupervised scene decomposition and representation</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>21</td>
<td><a href="https://arxiv.org/pdf/1901.07017.pdf">Spatial Broadcast Decoder</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>22</td>
<td><a href="https://arxiv.org/pdf/1903.00450.pdf">IODINE Network</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>23</td>
<td><a href="https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/datasets/make_mnistplus.py">Augmented MNIST data set, including texture-in-texture generation</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>24</td>
<td><a href="http://legacydirs.umiacs.umd.edu/~xyang35/files/understanding-variational-lower.pdf">Further explanation for VAE objective functino, ELBO and loss</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>25</td>
<td><a href="https://arxiv.org/pdf/1712.00636.pdf">Paper on using compressed representation of video data to truncate noise from low-frequency motion</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>26</td>
<td><a href="https://arxiv.org/pdf/1807.04689.pdf">Paper exploring the idea and implementation of general homemorphic manifolds extending the standard guassian prior</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>27</td>
<td><a href="https://arxiv.org/pdf/1703.06114.pdf">Paper creating an model which operates on clustering/classifying sets as objects rather than vectors</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>28</td>
<td><a href="https://arxiv.org/pdf/1702.08389.pdf">Paper on the equivariance of models (through parameter sharing) using resistance to group actions</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>29</td>
<td><a href="https://arxiv.org/pdf/1901.06082.pdf">"PROBABILISTIC SYMMETRY AND INVARIANT NEURAL NETWORKS.</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>30</td>
<td><a href="https://arxiv.org/pdf/1703.06211.pdf">"Deformable Convolutional Networks"</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>31</td>
<td><a href="https://arxiv.org/pdf/1609.04836.pdf">"ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA"</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
<tr>
<td>32</td>
<td><a href="http://www.cs.columbia.edu/~gravano/Papers/2017/tods17.pdf">Paper exploring (fast) time-series clustering</td>
<td>Entry</td>
<td>Workshop A</td>
</tr>
</tbody>
</table>
</div>
</section>
</article>
<!-- Footer -->
<footer id="footer">
<ul class="icons">
<li><a href="mailto:deeplearningatuga@gmail.com?Subject=Deep Learning @ UGA" class="icon fa-envelope-o"><span class="label">Email</span></a></li>
</ul>
<ul class="copyright">
<li>© EDS@UGA</li><li>Design: <a href="http://html5up.net">HTML5 UP</a></li>
</ul>
</footer>
</div>
<!-- Scripts -->
<script src="assets/js/jquery.min.js"></script>
<script src="assets/js/jquery.scrollex.min.js"></script>
<script src="assets/js/jquery.scrolly.min.js"></script>
<script src="assets/js/skel.min.js"></script>
<script src="assets/js/util.js"></script>
<!--[if lte IE 8]><script src="assets/js/ie/respond.min.js"></script><![endif]-->
<script src="assets/js/main.js"></script>
</body>
</html>