Call for Papers: ParLearning 2018
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Arindam Pal
Jan 24, 2018, 3:16:09 AM1/24/18
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The 7th International Workshop on Parallel and Distributed Computing
for Large Scale Machine Learning and Big Data Analytics
http://parlearning.ecs.fullerton.edu
May 21, 2018
In Conjunction with
The 32nd IEEE International Parallel & Distributed Processing Symposium
http://www.ipdps.org
May 21 - May 25, 2018
JW Marriott Parq Vancouver
Vancouver, British Columbia, Canada
****************************************************************************************
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
Recommender systems
Optimization algorithms (gradient descent, Newton methods)
Deep learning
Sampling/sketching techniques
Clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)
Classification (SVM and other classifiers)
SVD and other matrix computations
Probabilistic inference (Bayesian networks)
Logical reasoning
Graph algorithms/graph mining and knowledge graphs
Semi-supervised learning
Online/streaming learning
Generative adversarial networks
Using
Parallel architectures/frameworks (OpenMP, OpenCL, OpenACC, Intel TBB)
Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark)
Machine learning frameworks (TensorFlow, PyTorch, Theano, Caffe)
On
Clusters of conventional CPUs
Many-core CPU (e.g. Xeon Phi)
FPGA
Specialized ML accelerators (e.g. GPU and TPU)
Proceedings of the Parlearning workshop will be distributed at the conference and will be submitted for inclusion in the IEEE Xplore Digital Library after the conference.
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 IEEE IPDPS web page.
Important Dates
Paper submission: February 16, 2018 AoE
Notification: March 9, 2018
Camera Ready: March 16, 2018
Paper Guidelines
Submitted manuscripts should be upto 10 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references. Format requirements are posted on the IEEE IPDPS web page.
All submissions must be uploaded electronically.
Organization
General co-chairs: Henri Bal (Vrije Universiteit, The Netherlands) and Arindam Pal (TCS Research and Innovation, India)
Technical Program co-chairs: Azalia Mirhoseini (Google Brain, USA) and Thomas Parnell (IBM Research – Zurich, Switzerland)
Publicity chair: Yanik Ngoko (Université Paris XIII, France)
Steering Committee: Sutanay Choudhury (Pacific Northwest National Laboratory, USA), Anand Panangadan (California State University, Fullerton, USA), and Yinglong Xia (Huawei Research America, USA)
Technical Program Committee
Indrajit Bhattacharya, TCS Research, India
Vito Giovanni Castellana, Pacific Northwest National Laboratory, USA
Tanmoy Chakraborty, IIIT Delhi, India
Daniel Gerardo Chavarria, Pacific Northwest National Laboratory, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Zhihui Du, Tsinghua University, China
Anand Eldawy, University of Minnesota, USA
Erich Elsen, Google Brain, USA
Dinesh Garg, IIT Gandhinagar and IBM Research, India
Kripabandhu Ghosh, IIT Kanpur, India
Saptarshi Ghosh, IIT Kharagpur, India
Kazuaki Ishizaki, IBM Research - Tokyo, Japan
Farinaz Koushanfar, UCSD, USA
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Carson Leung, University of Manitoba, Canada
Animesh Mukherjee, IIT Kharagpur, India
Debnath Mukherjee, TCS Research, India
Francesco Parisi, University of Calabria, Italy
Saurabh Paul, PayPal, USA
Lijun Qian, Prairie View A&M University, USA
Lingfei Wu, IBM T. J. Watson Research Center, USA
Jianting Zhang, City College of New York, USA
The 7th International Workshop on Parallel and Distributed Computing
for Large Scale Machine Learning and Big Data Analytics
http://parlearning.ecs.fullerton.edu
May 21, 2018
In Conjunction with
The 32nd IEEE International Parallel & Distributed Processing Symposium
http://www.ipdps.org
May 21 - May 25, 2018
JW Marriott Parq Vancouver
Vancouver, British Columbia, Canada
****************************************************************************************
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
Recommender systems
Optimization algorithms (gradient descent, Newton methods)
Deep learning
Sampling/sketching techniques
Clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)
Classification (SVM and other classifiers)
SVD and other matrix computations
Probabilistic inference (Bayesian networks)
Logical reasoning
Graph algorithms/graph mining and knowledge graphs
Semi-supervised learning
Online/streaming learning
Generative adversarial networks
Using
Parallel architectures/frameworks (OpenMP, OpenCL, OpenACC, Intel TBB)
Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark)
Machine learning frameworks (TensorFlow, PyTorch, Theano, Caffe)
On
Clusters of conventional CPUs
Many-core CPU (e.g. Xeon Phi)
FPGA
Specialized ML accelerators (e.g. GPU and TPU)
Proceedings of the Parlearning workshop will be distributed at the conference and will be submitted for inclusion in the IEEE Xplore Digital Library after the conference.
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 IEEE IPDPS web page.
Important Dates
Paper submission: February 16, 2018 AoE
Notification: March 9, 2018
Camera Ready: March 16, 2018
Paper Guidelines
Submitted manuscripts should be upto 10 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references. Format requirements are posted on the IEEE IPDPS web page.
All submissions must be uploaded electronically.
Organization
General co-chairs: Henri Bal (Vrije Universiteit, The Netherlands) and Arindam Pal (TCS Research and Innovation, India)
Technical Program co-chairs: Azalia Mirhoseini (Google Brain, USA) and Thomas Parnell (IBM Research – Zurich, Switzerland)
Publicity chair: Yanik Ngoko (Université Paris XIII, France)
Steering Committee: Sutanay Choudhury (Pacific Northwest National Laboratory, USA), Anand Panangadan (California State University, Fullerton, USA), and Yinglong Xia (Huawei Research America, USA)
Technical Program Committee
Indrajit Bhattacharya, TCS Research, India
Vito Giovanni Castellana, Pacific Northwest National Laboratory, USA
Tanmoy Chakraborty, IIIT Delhi, India
Daniel Gerardo Chavarria, Pacific Northwest National Laboratory, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Zhihui Du, Tsinghua University, China
Anand Eldawy, University of Minnesota, USA
Erich Elsen, Google Brain, USA
Dinesh Garg, IIT Gandhinagar and IBM Research, India
Kripabandhu Ghosh, IIT Kanpur, India
Saptarshi Ghosh, IIT Kharagpur, India
Kazuaki Ishizaki, IBM Research - Tokyo, Japan
Farinaz Koushanfar, UCSD, USA
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Carson Leung, University of Manitoba, Canada
Animesh Mukherjee, IIT Kharagpur, India
Debnath Mukherjee, TCS Research, India
Francesco Parisi, University of Calabria, Italy
Saurabh Paul, PayPal, USA
Lijun Qian, Prairie View A&M University, USA
Lingfei Wu, IBM T. J. Watson Research Center, USA
Jianting Zhang, City College of New York, USA
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