[image 02336] SNL-2017 Workshop 3rd CFP

[Yasushi Makihara]

イメージメーリングリストの皆様，
（※重複して受信された方はご容赦下さい．）

大阪大学の槇原と申します．

古井貞熙先生の代理で，7月に開催されるSNL-2017のCFPを送付させて頂きます．
多数の論文の投稿を，宜しくお願い致します．

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Title: 3rd CFP "International Workshop on Symbolic-Neural Learning”

* Apologies if you receive multiple copies of this CFP.

Dear Colleagues,

We are glad to announce that the First International Workshop on  
Symbolic-Neural Learning (SNL-2017) will be held in Nagoya, Japan, July  
7-8, 2017.

Important Dates (any time zone):
March 15, 2017  Paper submission deadline
                 (c.f . http://www.ttic.edu/SNL2017/submissions.htm)
May 10, 2017    Notification of acceptance
June 7, 2017    Camera-ready submission deadline
June 9, 2017    Early registration deadline
July 7-8, 2017  SNL-2017 at Nagoya Congress Center, Nagoya, Japan

Symbolic-neural learning involves deep learning methods in combination  
with symbolic structures. A "deep learning method" is taken to be a  
learning process based on gradient descent on real-valued model  
parameters. A "symbolic structure" is a data structure involving symbols  
drawn from a large vocabulary; for example, sentences of natural  
language, parse trees over such sentences, databases (with entities  
viewed as symbols), and the symbolic expressions of mathematical logic  
or computer programs.　Natural applications of symbolic-neural learning  
include, but are not limited to, the following areas:

- Image caption generation and visual question answering
- Speech and natural language interactions in robotics
- Machine translation
- General knowledge question answering
- Reading comprehension
- Textual entailment
- Dialogue systems

Various architectural ideas are shared by deep learning systems across  
these areas. These include word and phrase embeddings, recurrent neural  
networks (LSTMs and GRUs) and various attention and memory mechanisms.  
Certain linguistic and semantic resources may also be relevant across  
these applications. For example dictionaries, thesauri, WordNet,  
FrameNet, FreeBase, DBPedia, parsers, named entity recognizers,  
coreference systems, knowledge graphs and encyclopedias. Deep learning  
approaches to the above application areas, with architectures and tools  
subjected to quantitative evaluation, loosely define the focus of the  
workshop.

We invite submissions of abstracts and high-quality, original papers  
within the workshop focus. The workshop will consist of a half-day of  
invited talks and a full day of presentations of accepted papers.

Keynote speakers include:

Yoshua Bengio (invited) Université de Montréal, Montréal, Canada
William Cohen (invited) Carnegie Mellon University, Pittsburgh, USA
Masashi Sugiyama RIKEN and University of Tokyo, Tokyo, Japan
Jun'ichi Tsujii AI Center, AIST, Tokyo, Japan

Organizing Committee:

Sadaoki Furui Toyota Technological Institute at Chicago, Chicago, USA
Tomoko Matsui Institute of Statistical Mathematics, Tokyo, Japan
David McAllester Toyota Technological Institute at Chicago, Chicago, USA
Yutaka Sasaki Toyota Technological Institute, Nagoya, Japan
Koichi Shinoda Tokyo Institute of Technology, Tokyo, Japan
Masashi Sugiyama RIKEN and University of Tokyo, Tokyo, Japan
Jun'ichi Tsujii AI Center, AIST, Tokyo, Japan

Other details can be found at the web site: http://www.ttic.edu/SNL2017/

Best regards,
--
David McAllester, Toyota Technological Institute at Chicago
Yutaka Sasaki, Toyota Technological Institute
SNL-2017 Program Co-chairs
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槇原 靖 <makihara ＠ am.sanken.osaka-u.ac.jp>
大阪大学 産業科学研究所 第1研究部門(情報科学系)
複合知能メディア研究分野　八木研究室　准教授
tel. 06-6879-8422, fax. 06-6877-4375
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