[image 02303] SNL-2017 Workshop 3rd CFP

[Ikuhisa Mitsugami]

Image-ML, Robotics-MLの皆様，
（※重複して受信された方はご容赦下さい．）

大阪大学の満上と申します．
以下，本学八木先生の代理で投稿いたします．


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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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