The 8th International Workshop on Parallel and Distributed Computing for Large-Scale Machine Learning and Big Data Analytics
ParLearning 2019
- URL: https://parlearning.github.io
- Event Date: 2019-08-05 ~ 2019-08-05
- Submission Date: 2019-05-05
- Location: Anchorage, United States of America
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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
**************************************************************
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.
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, USA), and Yinglong Xia (Huawei Research America, USA)
Technical Program Committee
o Vito Giovanni Castellana, PNNL, USA
o Daniel Gerardo Chavarria, PNNL, USA
o Jianting Zhang, City College of New York, USA
o Mark Fox, University of Toronto, Canada
o Dinesh Garg, IBM Research, India
o Animesh Mukherjee, IIT Kharagpur, India
o Francesco Parisi, University of Calabria, Italy
o Farinaz Koushanfar, UCSD, USA
o Erich Elsen, Google Brain, USA
o Kazuaki Ishizaki, IBM Research - Tokyo, Japan
o Zhihui Du, Tsinghua University, China
o Anand Eldawy, University of Minnesota, USA
o Carson Leung, University of Manitoba, Canada
o Lingfei Wu, IBM Watson Research Center, USA
o Arnab Bhattacharya, IIT Kanpur, India
o Saptarshi Ghosh, IIT Kharagpur, India
o Kripabandhu Ghosh, IIT Kanpur, India
o Tanmoy Chakraborty, IIIT Delhi, India
o Mayank Singh, IIT Gandhinagar, India
Past Workshops
The first 7 editions of ParLearning were organized in conjunction with the International Parallel and Distributed Processing Symposium (IPDPS). The details of the past workshops can be found on the website http://parlearning.ecs.fullerton.edu. From this year, the organizers have decided to conduct it with KDD.
* 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
**************************************************************
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.
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, USA), and Yinglong Xia (Huawei Research America, USA)
Technical Program Committee
o Vito Giovanni Castellana, PNNL, USA
o Daniel Gerardo Chavarria, PNNL, USA
o Jianting Zhang, City College of New York, USA
o Mark Fox, University of Toronto, Canada
o Dinesh Garg, IBM Research, India
o Animesh Mukherjee, IIT Kharagpur, India
o Francesco Parisi, University of Calabria, Italy
o Farinaz Koushanfar, UCSD, USA
o Erich Elsen, Google Brain, USA
o Kazuaki Ishizaki, IBM Research - Tokyo, Japan
o Zhihui Du, Tsinghua University, China
o Anand Eldawy, University of Minnesota, USA
o Carson Leung, University of Manitoba, Canada
o Lingfei Wu, IBM Watson Research Center, USA
o Arnab Bhattacharya, IIT Kanpur, India
o Saptarshi Ghosh, IIT Kharagpur, India
o Kripabandhu Ghosh, IIT Kanpur, India
o Tanmoy Chakraborty, IIIT Delhi, India
o Mayank Singh, IIT Gandhinagar, India
Past Workshops
The first 7 editions of ParLearning were organized in conjunction with the International Parallel and Distributed Processing Symposium (IPDPS). The details of the past workshops can be found on the website http://parlearning.ecs.fullerton.edu. From this year, the organizers have decided to conduct it with KDD.
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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