-
Notifications
You must be signed in to change notification settings - Fork 1
/
test.html~
executable file
·204 lines (170 loc) · 8.03 KB
/
test.html~
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
<!DOCTYPE html >
<html >
<head>
<title>Hugo Larochelle</title>
<meta http-equiv="content-type" content="text/html; charset=iso-8859-1" />
<link rel="stylesheet" href="css/2.css" type="text/css" media="screen,projection" />
</head>
<body>
<div id="wrapper">
<div id="innerwrapper">
<div id="header">
Hugo Larochelle
<!-- <h1 id="header-right"></h1>-->
<ul id="nav">
<li><a href="index_en.html" accesskey="H" class="active"><em>H</em>ome</a></li>
<li><a href="publications_en.html" accesskey="p"><em>P</em>ublications</a></li>
<li><a href="university_en.html" accesskey="u"><em>U</em>niversity</a></li>
<li><a href="links_en.html" accesskey="l"><em>L</em>inks</a></li>
<li><a href="index_fr.html" accesskey="f"><em>F</em>rench</a></li>
</ul>
</div>
<div id="sidebar">
<h2>Recent stuff</h2>
<!--
<table border="0" cellspacing="3" cellpadding="0" style="BORDER: #666 2px groove;" bgcolor="#DDD">
<td> <font color="#555">
<b>I'm looking for PhD and master students.</b> <br>
If you are interested in machine learning research,
please contact me for details.<br>
Learn about USherbrooke <a href="http://www.usherbrooke.ca/accueil/english/">here</a>.
</font>
</td>
</table>
<br>
-->
<b> Thanks Google for the <br><a href="http://services.google.com/fh/files/blogs/research_awards_recipients_july_2012.pdf">Google Research Award</a></b><br>
<br>
<b> Co-organizor of upcoming <a href="https://sites.google.com/site/representationworkshopicml2012/">Representation Learning Workshop</a> at <a href="http://icml.cc/2012/">ICML 2012</a></b><br>
<br>
<b>On the senior program committee for <a href="http://www.auai.org/uai2012/"> UAI 2012</a></b><br>
<br>
<b>Guest editor for TPAMI Special Issue on <i>Learning Deep Architectures</i> [<a href="http://www.computer.org/cms/Computer.org/transactions/cfps/cfp_tp_lda.pdf">cfp</a>]</b><br>
<br>
<b> MLPython [<a href="https://bitbucket.org/HugoLarochelle/mlpython/overview" target=_top>bitbucket</a>] [<a href="http://www.dmi.usherb.ca/~larocheh/mlpython/" target=_top>doc</a>] </b><br>
<br>
<b>Nonparametric Guidance of Autoencoder Representations using Label Information [<a href="publications/snoek12a.pdf" target=_top>pdf</a>]</b><br>
Jasper Snoek, Ryan P. Adams and Hugo Larochelle,<br>
JMLR, 2012<br><br>
<b>A Neural Autoregressive Topic Model [pdf]</b><br>
Hugo Larochelle and Stanislas Lauly<br>
NIPS, 2012<br><br>
<b>Practical Bayesian Optimization of Machine Learning Algorithms [pdf]</b><br>
Jasper Snoek, Hugo Larochelle and Ryan P. Adams,<br>
NIPS, 2012<br><br>
<b> Learning to Rank By Aggregating Expert Preferences [<a href="publications/cikm_2012.pdf" target=_top>pdf</a>]</b><br>
Maksims Volkovs, Hugo Larochelle and Richard Zemel,<br>
CIKM, 2012<br><br>
<b>Learning Where to Attend With Deep Architectures for Image Tracking [<a href="http://arxiv.org/pdf/1109.3737v1.pdf" target=_top>pdf</a>]</b><br>
Misha Denil, Loris Bazzani, Hugo Larochelle and Nando de Freitas,<br>
Neural Computation, 2012<br>
<br>
<b> Training Restricted Boltzmann Machines on Word Observations [<a href="publications/wrrbm_icml2012.pdf" target=_top>pdf</a>]</b><br>
George E. Dahl, Ryan P. Adams and Hugo Larochelle,<br>
ICML, 2012<br>
<br>
<b> Learning Algorithms for the Classiffcation Restricted Boltzmann Machine [<a href="publications/larochelle12a.pdf" target=_top>pdf</a>]</b><br>
Hugo Larochelle, Michael Mandel, Razvan Pascanu and Yoshua Bengio,<br>
JMLR, 2012<br>
<br>
<b> On Nonparametric Guidance for Learning Autoencoder Representations [<a href="publications/aistats_2012.pdf" target=_top>pdf</a>]</b><br>
Jasper Snoek, Ryan P. Adams and Hugo Larochelle,<br>
AISTATS, 2012<br>
</div>
<!--
<div id="sidebarright">
</div>
-->
<div id="contentnorightbar">
<table width="520" border="0" cellspacing="0" cellpadding="0" height="34">
<tr>
<td>
<b><font size=+1>Hugo Larochelle</font></b><br>
Assistant Professor<br>
Member of the <a href="http://www.moivre.usherbrooke.ca/">MOIVRE</a> research center<br>
<a href="http://www.usherbrooke.ca/informatique/">Département d'informatique</a><br>
<a href="http://www.usherbrooke.ca/">Université de
Sherbrooke</a><br><br>
<b>Email:</b> hugo [dot] larochelle [at] usherbrooke [dot] ca <br>
<b>Phone:</b> (819) 821-8000 # 66121 <br>
<b>Fax:</b> (819) 821-8200 <br>
<b>Office:</b> D4-1024-1 <br>
<b>Address:</b> 2500 boul. de l'Université, Sherbrooke (QC), Canada, J1K 2R1
</td>
<td> <img src="images/photo-larocheh.jpg" WIDTH=120 align=top> </td>
</tr>
</table><br>
<h2>Research interests</h2> My research focuses
on <a href="http://en.wikipedia.org/wiki/Machine_learning">machine
learning</a>, i.e. in the development of algorithms
capable of extracting concepts and abstractions from data.
I'm particularly interested in deep and probabilistic
neural networks, applied to problems related to artificial
intelligence such as computer vision and natural language
processing.
<br><br>
More specifically, my research mainly addresses the following topics:
<br><br>
<ul>
<li> <b>Tasks:</b> supervised, semi-supervised and unsupervised learning,
structured output prediction, ranking, density estimation;
<li> <b>Models:</b> deep learning, neural networks, autoencoders,
Boltzmann machines, Markov random fields;
<li> <b>Applications:</b> object recognition and
tracking, document classification, information
retrieval;
</ul>
<h2>Research projects</h2>
Here is a short list of my current projects:
<br><br>
<ul>
<li> <b><a href="projects_attention.html">Attention-based computer vision:</a></b> development of
computer vision systems relying on attentional mechanisms;
<br><br>
<li> <b><a href="projects_deep_learning.html">Deep learning:</a></b> exploration of learning
procedures and algorithms to better train neural
networks with several hidden layers;
<br><br>
<li> <b><a href="projects_classrbm.html">Restricted Boltzmann machines for
classification:</a></b> investigation of variants of the
restricted Boltzmann machine that are more suitable
for classification problems in high dimensions
(images, documents, etc.);
<br><br>
<li> <b><a href="projects_struct_output.html">Learning algorithms for structured output
prediction:</a></b> development of new learning
algorithms for structured output prediction, with a
focus on energy-based models;
<br><br>
<li> <b><a href="projects_nade.html">Neural autoregressive models:</a></b> investigation of
neural network-based solutions to the modelling of the distribution
of high-dimensional data.
<br>
</ul>
<br>
<h2> Mini-CV</h2>
Before joining the Computer Science department
of <a href="http://www.usherbrooke.ca/">Université de
Sherbrooke</a>, I spent two years in
the <a href="http://learning.cs.toronto.edu/">machine
learning group</a>
at <a href="http://www.utoronto.ca">University of
Toronto</a>, where I was a postdoctoral fellow under the supervision of
<a href="http://www.cs.toronto.edu/~hinton/">Geoffrey
Hinton</a>.
<br><br>
I obtained my Ph.D. in Computer Science at
<a href="http://www.umontreal.ca/">Université de
Montréal</a>, under the supervision of
professor <a href="http://www.iro.umontreal.ca/~bengioy">Yoshua
Bengio</a>. I also did my undergrad at Université de Montréal,
in Mathematics and Computer Science.
<br><br> My complete CV can be found
<a href="CV_en.pdf">here</a>.
</div>
<div id="footer">
</div>
</div>
</div>
</body>
</html>