Connectionists: Second CFP: Deep Structured Prediction Workshop at ICML2017

[Bert Huang]

All,

This is a reminder that the submission deadline for the Deep Structured Prediction Workshop is this Friday, June 2nd. 

Best wishes,
Bert

--
Bert Huang, Ph.D. 
Assistant Professor
Dept. of Computer Science, Virginia Tech
http://berthuang.com

=== Call for papers:  Deep Structured Prediction === 
Workshop of the International Conference on Machine Learning (ICML) 2017 
Sydney, Australia 
11 August 2017
Website: https://deepstruct.github.io/ICML17 <https://deepstruct.github.io/ICML17>
TL;DR: 4 pages, in ICML format, submit by June 2nd PT.
  
Deep learning has revolutionized machine learning for many domains and problems. Today, most successful applications of deep learning involve predicting single variables (like univariate regression or multi-class classification). However, many real problems involve highly dependent, structured variables. In such scenarios, it is desired or even necessary to model correlations and dependencies between the multiple input and output variables. Such problems arise in a wide range of domains, from natural language processing, computer vision, computational biology and others. 

Some approaches to these problems directly use deep learning concepts, such as those that generate sequences using recurrent neural networks or that output image segmentations through convolutions. Others adapt the concepts from structured output learning. These structured output prediction problems were traditionally handled using linear models and hand-crafted features, with a structured optimization such as inference. It has recently been proposed to combine the representational power of deep neural networks with modeling variable dependence in a structured prediction framework. There are numerous interesting research questions related to modeling and optimization that arise in this problem space.

The workshop will bring together experts in machine learning and application domains whose research focuses on combining deep learning and structured models. Specifically, it will provide an overview of existing approaches from various domains to distill from their success principles that can be more generally applicable. We will also discuss the main challenges arising in this setting and outline potential directions for future progress. The target audience consists of researchers and practitioners in machine learning and application areas. 
    
We invite the submission of short papers no longer than four pages, including references, addressing machine learning research that intersects structured prediction and deep learning, including any of the following topics: 
Deep learning approaches for structured-output problems
Integration of deep learning with structured-output learning
End-to-end learning of probabilistic models with non-linear potentials
Deep learning applications with dependent inputs or outputs

Papers should be formatted according to the ICML template: 
(http://media.nips.cc/Conferences/ICML2017/icml2017.tgz <http://media.nips.cc/Conferences/ICML2017/icml2017.tgz>). 
Only papers using the above template will be considered.  Word templates will not be provided. 
  
Papers should be submitted through easychair at the following address: 
https://easychair.org/conferences/?conf=1stdeepstructws <https://easychair.org/conferences/?conf=1stdeepstructws> 

Papers will be reviewed for relevance and quality. Accepted papers will be posted online. Authors of high-quality papers will be offered oral presentations at the workshop, and we will award a best-paper and runner-up prize.
  
=== Important Dates === 
***Submission deadline: June 2, 2017
***Notification of acceptance:  June 18, 2017 
***Camera-ready deadline:  August 1, 2017 

=== Program committee === 
David Belanger, University of Massachusetts Amherst
Matthew Blaschko, KU Leuven
Ryan Cotterell, Johns Hopkins University
Ming-Wei Chang, Microsoft Research
Raia Hadsell, Google DeepMind
Hal Daumé III, University of Maryland
Justin Domke, University of Massachusetts Amherst
Andrew McCallum, University of Massachusetts Amherst
Eliyahu Kiperwasser, Bar-Ilan University
Jason Naradowsky, University of Cambridge
Sebastian Nowozin, Microsoft Research, Cambridge, UK
Nanyun Peng, Johns Hopkins University
Amirmohammad Rooshenas, University of Oregon
Dan Roth, University of Illinois at Urbana-Champaign
Alexander Rush, Harvard University 
Sameer Singh, University of California Irvine
Uri Shalit, New York University
Andreas Vlachos, University of Sheffield
Yi Yang, Georgia Institute of Technology
Scott Yih, Microsoft Research
Yangfeng Ji, University of Washington
Yisong Yue, California Institute of Technology
Shuai Zheng, eBay

  
=== Workshop Organizers === 
Isabelle Augenstein, University College London
Kai-Wei Chang, University of California Los Angeles
Gal Chechik, Bar-Ilan University / Google
Bert Huang, Virginia Tech
André Martins, Unbabel and Instituto de Telecomunicacoes
Ofer Meshi, Google
Alexander Schwing, University of Illinois Urbana-Champaign




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