DAUPHIN.IO: Keyword Density Checker

Total words: 1503 | 2-word phrases: 393 | 3-word phrases: 429 | 4-word phrases: 440

PAGE INFO

Title	Try to keep the title under 60 characters (15 characters) Yann's Homepage
Description	Try to keep the meta description between 50 - 160 characters (0 characters)
Keywords	Meta keywords are not recommended anymore (0 characters)
H1	H1 tag on the page (6 characters)

#	Keyword	Frequency	Density
1	broken image best	3	0.70%
2	image best paper	3	0.70%
3	published in international	2	0.47%
4	sequence to sequence	2	0.47%
5	neural information processing	2	0.47%
6	conference on machine	2	0.47%
7	paper honorable mention	2	0.47%
8	on machine learning	2	0.47%
9	best paper honorable	2	0.47%
10	transfer learning challenge	2	0.47%
11	unsupervised transfer learning	2	0.47%
12	international conference on	2	0.47%
13	the loss surface	2	0.47%
14	talks broken image	2	0.47%
15	in this work	1	0.23%
16	neural networks compared	1	0.23%
17	on machine learning	1	0.23%
18	on convolutional neural	1	0.23%
19	convolutional neural networks	1	0.23%
20	learning published in	1	0.23%
21	sequence learning published	1	0.23%
22	to sequence learning	1	0.23%
23	examples broken image	1	0.23%
24	convolutional sequence to	1	0.23%
25	learning 2017 the	1	0.23%
26	between examples broken	1	0.23%
27	linear between examples	1	0.23%
28	be linear between	1	0.23%
29	to be linear	1	0.23%
30	network to be	1	0.23%

#	Keyword	Frequency	Density
1	broken image best paper	3	0.68%
2	unsupervised transfer learning challenge	2	0.45%
3	image best paper honorable	2	0.45%
4	conference on machine learning	2	0.45%
5	best paper honorable mention	2	0.45%
6	learning published in international	1	0.23%
7	sequence learning published in	1	0.23%
8	to sequence learning published	1	0.23%
9	sequence to sequence learning	1	0.23%
10	convolutional sequence to sequence	1	0.23%
11	between examples broken image	1	0.23%
12	conference on machine learning	1	0.23%
13	linear between examples broken	1	0.23%
14	be linear between examples	1	0.23%
15	to be linear between	1	0.23%
16	network to be linear	1	0.23%
17	the network to be	1	0.23%
18	encouraging the network to	1	0.23%
19	alleviate these issues by	1	0.23%
20	to alleviate these issues	1	0.23%
21	mixup to alleviate these	1	0.23%
22	international conference on machine	1	0.23%
23	thumbnail image broken image	1	0.23%
24	principle called mixup to	1	0.23%
25	translation was recurrent neural	1	0.23%
26	entirely on convolutional neural	1	0.23%
27	a stateoftheart architecture based	1	0.23%
28	introduced a stateoftheart architecture	1	0.23%
29	we introduced a stateoftheart	1	0.23%
30	work we introduced a	1	0.23%
31	this work we introduced	1	0.23%
32	in this work we	1	0.23%
33	networks in this work	1	0.23%
34	as translation was recurrent	1	0.23%
35	on machine learning 2017	1	0.23%
36	such as translation was	1	0.23%
37	problems such as translation	1	0.23%
38	sequence problems such as	1	0.23%
39	to sequence problems such	1	0.23%
40	sequence to sequence problems	1	0.23%

#	URL	Whois	Check
1	http://arxiv.org/pdf/1406.2572.pdf	Whois	arxiv.org
2	https://www.wired.com/2017/05/facebook-open-sources-neural-networks-speed-translations/	Whois	wired.com
3	http://www.causality.inf.ethz.ch/unsupervised-learning.php	Whois	ethz.ch
4	https://uploads.strikinglycdn.com/files/e44e5773-49b5-4479-94bb-13f89f2ab11f/Resume.pdf?id=3864481	Whois	strikinglycdn.com
5	https://scholar.google.com/citations?user=XSforroAAAAJ&hl=en	Whois	google.com
6	https://papers.nips.cc/paper/5486-identifying-and-attacking-the-saddle-point-problem-in-high-dimensional-non-convex-optimization.pdf	Whois	nips.cc
7	https://openreview.net/pdf?id=r1Ddp1-Rb	Whois	openreview.net
8	http://proceedings.mlr.press/v70/gehring17a/gehring17a.pdf	Whois	mlr.press
9	https://signalprocessingsociety.org/community-involvement/award-recipients	Whois	signalprocessingsociety.org
10	https://nips.cc/Conferences/2011/Awards	Whois	nips.cc
11	http://www.causality.inf.ethz.ch/unsupervised-learning.php	Whois	ethz.ch
12	https://acl2018.org/2018/07/26/hon-mention-papers	Whois	acl2018.org
13	https://cs.anu.edu.au/few/emotiw.html	Whois	edu.au
14	https://www.wired.com/2017/05/facebook-open-sources-neural-networks-speed-translations/	Whois	wired.com
15	http://techtalks.tv/talks/tangent-propagation/63036/	Whois	techtalks.tv
16	http://videolectures.net/nips2011_dauphin_manifold/	Whois	videolectures.net