posted by organizer: youngsr || 7402 views || tracked by 7 users: [display]

MLHPC 2016 : Machine Learning in High Performance Computing Environments Workshop

FacebookTwitterLinkedInGoogle

Link: http://ornlcda.github.io/MLHPC2016/
 
When Nov 14, 2016 - Nov 14, 2016
Where Salt Lake City, UT, USA
Submission Deadline Jul 30, 2016
Notification Due Sep 1, 2016
Final Version Due Sep 15, 2016
Categories    machine learning   deep learning   high performance computing   supercomputing
 

Call For Papers

Call for Papers

The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for machine learning. This workshop will focus on the greatest challenges in utilizing HPC for machine learning and methods for exploiting data parallelism, model parallelism, ensembles, and parameter search. We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning and to share the wide range of applications that would benefit from HPC powered machine learning.

In recent years, the models and data available for machine learning (ML) applications have grown dramatically. High performance computing (HPC) offers the opportunity to accelerate performance and deepen understanding of large data sets through machine learning. Current literature and public implementations focus on either cloud-­‐based or small-­‐scale GPU environments. These implementations do not scale well in HPC environments due to inefficient data movement and network communication within the compute cluster, originating from the significant disparity in the level of parallelism. Additionally, applying machine learning to extreme scale scientific data is largely unexplored. To leverage HPC for ML applications, serious advances will be required in both algorithms and their scalable, parallel implementations.
Topics will include but will not be limited to:

Machine learning models, including deep learning, for extreme scale systems
Enhancing applicability of machine learning in HPC (e.g. feature engineering, usability)
Learning large models/optimizing hyper parameters (e.g. deep learning, representation learning)
Facilitating very large ensembles in extreme scale systems
Training machine learning models on large datasets and scientific data
Overcoming the problems inherent to large datasets (e.g. noisy labels, missing data, scalable ingest)
Applications of machine learning utilizing HPC
Future research challenges for machine learning at large scale.
Large scale machine learning applications

Authors are invited to submit full papers with unpublished, original work of not more than 8 pages. All papers should be formatted using the ACM style (see http://www.acm.org/sigs/publications/proceedings-templates). All accepted papers (subject to post-review revisions) will be published in the ACM digital and IEEE Xplore libraries by ACM SIGHPC. Papers should be submitted using EasyChair at: https://www.easychair.org/conferences/?conf=mlhpc2016.

This workshop is being held at SC16. http://sc16.supercomputing.org/

Related Resources

IEEE COINS 2022   IEEE COINS 2022: Hybrid (3 days on-site | 2 days virtual)
Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
IEEE COINS 2022   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems |
MLDM 2022   18th International Conference on Machine Learning and Data Mining
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
ICML 2022   39th International Conference on Machine Learning
ICADCML 2022   3rd International Conference on Advances in Distributed Computing and Machine Learning - 2022
CFDSP 2022   2022 International Conference on Frontiers of Digital Signal Processing (CFDSP 2022)
HPCCT--ACM, EI, Scopus 2022   ACM--2022 6th High Performance Computing and Cluster Technologies Conference (HPCCT 2022)--EI Compendex, Scopus
ICDLT--ACM, Ei, Scopus 2022   ACM--2022 6th International Conference on Deep Learning Technologies (ICDLT 2022)--Ei Compendex, Scopus