Connectionists: CFP: ParLearning 2019 in conjunction with KDD 2019

[Arindam Pal]

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* The 8th International Workshop on Parallel and Distributed Computing for
* Large-Scale Machine Learning and Big Data Analytics (ParLearning 2019)
* https://parlearning.github.io
* August 5, 2019
* Anchorage, Alaska, USA
*
* Co-located with
* The 25th ACM SIGKDD International Conference on
* Knowledge Discovery and Data Mining (KDD 2019)
* https://www.kdd.org/kdd2019/
* August 4 - August 8, 2019
* Dena’ina Convention Center and William Egan Convention Center
* Anchorage, Alaska, USA
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Call for Papers

Scaling up machine-learning (ML), data mining (DM) and reasoning algorithms
from Artificial Intelligence (AI) for massive datasets is a major technical
challenge in the time of "Big Data". The past ten years have seen the rise
of multi-core and GPU based computing. In parallel and distributed
computing, several frameworks such as OpenMP, OpenCL, and Spark continue to
facilitate scaling up ML/DM/AI algorithms using higher levels of
abstraction. We invite novel works that advance the trio-fields of ML/DM/AI
through development of scalable algorithms or computing frameworks. Ideal
submissions should describe methods for scaling up X using Y on Z, where
potential choices for X, Y and Z are provided below.

Scaling up

o Recommender systems
o Optimization algorithms (gradient descent, Newton methods)
o Deep learning
o Distributed algorithms and AI for Blockchain
o Sampling/sketching techniques
o Clustering (agglomerative techniques, graph clustering, clustering
heterogeneous data)
o Classification (SVM and other classifiers)
o SVD and other matrix computations
o Probabilistic inference (Bayesian networks)
o Logical reasoning
o Graph algorithms, graph mining and knowledge graphs
o Semi-supervised learning
o Online and streaming learning
o Generative adversarial networks

Using

o Parallel architectures/frameworks (OpenMP, OpenCL, OpenACC, Intel TBB)
o Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark)
o Machine learning frameworks (TensorFlow, PyTorch, Theano, Caffe)

On

o Clusters of conventional CPUs
o Many-core CPU (e.g. Xeon Phi)
o FPGA
o Specialized ML accelerators (e.g. GPU and TPU)

Workshop Proceedings

Accepted papers will be published in the conference proceedings by ACM and
also appear in the ACM Digital Library.

Awards

Best Paper Award: The program committee will nominate a paper for the Best
Paper award. In past years, the Best Paper award included a cash prize.
Stay tuned for this year!
Travel awards: Students with accepted papers have a chance to apply for a
travel award. Please find details on the ACM KDD 2019 web page.

Important Dates

o Paper submission: May 5, 2019 (Anywhere on Earth)
o Author notification: June 1, 2019
o Camera-ready version: June 8, 2019

Paper Guidelines

Submissions are limited to a total of 10 pages, including all content and
references, and must be in PDF format and formatted according to the new
Standard ACM Conference Proceedings Template. Additional information about
formatting and style files is available online at:
https://www.acm.org/publications/proceedings-template. Papers that do not
meet the formatting requirements will be rejected without review.

All submissions must be uploaded electronically at
https://www.easychair.org/conferences/?conf=parlearning2019.

Keynote Speakers

o Professor V.S. Subrahmanian (Dartmouth College, Hanover, NH, USA)

Organizing Committee

o General Chairs: Arindam Pal (TCS Research and Innovation, Kolkata, India)
and Henri Bal (Vrije Universiteit, Amsterdam, Netherlands)
o Program Chairs: Azalia Mirhoseini (Google AI, Mountain View, CA, USA),
Thomas Parnell (IBM Research, Zurich, Switzerland)
o Publicity Chair: Anand Panangadan (California State University,
Fullerton, USA)
o Steering Committee Chairs: Sutanay Choudhury (Pacific Northwest National
Laboratory, Richland, WA, USA) and Yinglong Xia (Huawei Research America,
Santa Clara, CA, USA)

Regards,
Arindam Pal, Ph.D.
Research Scientist
TCS Research and Innovation
http://www.cse.iitd.ac.in/~arindamp/
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