人工智能 | UAI 2019等国际会议信息4条

人工智能

ScaDL 2019

Scalable Deep Learning over Parallel and Distributed Infrastructures

全文截稿: 2019-01-25

开会时间: 2019-05-24

会议难度: ★★

CCF分类: 无

会议地点: Rio de Janeiro

网址：https://sites.google.com/site/scadlworkshop/

In this workshop we solicit research papers focused on distributed deep learning aiming to achieve efficiency and scalability for deep learning jobs over distributed and parallel systems. Papers focusing both on algorithms as well as systems are welcome. We invite authors to submit papers on topics including but not limited to:  
   Deep learning on HPC systems  
   Deep learning for edge devices  
   Model-parallel and data-parallel techniques  
   Asynchronous SGD for Training DNNs  
   Communication-Efficient Training of DNNs  
   Model/data/gradient compression  
   Learning in Resource constrained environments  
   Coding Techniques for Straggler Mitigation  
   Elasticity for deep learning jobs/spot market enablement  
   Hyper-parameter tuning for deep learning jobs  
   Hardware Acceleration for Deep Learning  
   Scalability of deep learning jobs on large number of nodes  
   Deep learning on heterogeneous infrastructure  
   Efficient and Scalable Inference  
   Data storage/access in shared networks for deep learning jobs

人工智能

FastPath 2019

International Workshop on Performance Analysis of Machine Learning Systems

全文截稿: 2019-02-08

开会时间: 2019-03-24

会议难度: ★

CCF分类: 无

会议地点: Madison, Wisconsin, USA

网址：https://tinyurl.com/2019-FastPath

FastPath 2019 brings together researchers and practitioners involved in cross-stack hardware/software performance analysis, modeling, and evaluation for efficient machine learning systems. Machine learning demands tremendous amount of computing. Current machine learning systems are diverse, including cellphones, high performance computing systems, database systems, self-driving cars, robotics, and in-home appliances. Many machine-learning systems have customized hardware and/or software. The types and components of such systems vary, but a partial list includes traditional CPUs assisted with accelerators (ASICs, FPGAs, GPUs), memory accelerators, I/O accelerators, hybrid systems, converged infrastructure, and IT appliances. Designing efficient machine learning systems poses several challenges.
These include distributed training on big data, hyper-parameter tuning for models, emerging accelerators, fast I/O for random inputs, approximate computing for training and inference, programming models for a diverse machine-learning workloads, high-bandwidth interconnect, efficient mapping of processing logic on hardware, and cross system stack performance optimization. Emerging infrastructure supporting big data analytics, cognitive computing, large-scale machine learning, mobile computing, and internet-of-things, exemplify system designs optimized for machine learning at large.

人工智能

UAI 2019

International Conference on Uncertainty in Artificial Intelligence

摘要截稿: 2019-03-04

全文截稿: 2019-03-08

开会时间: 2019-07-22

会议难度: ★★★★

CCF分类: B类

会议地点: Tel Aviv, Israel

网址：http://auai.org/uai2019/cfp.php

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. UAI 2019 will be held in Tel Aviv, Israel, on July 22-26, 2019.

UAI solicits submission of papers which describe novel theories, methodology and applications related to knowledge representation, learning, and reasoning under uncertainty. A non-exclusive list of subject areas can be found here. We welcome submissions by authors who are new to the UAI conference, or on new and emerging topics. We encourage submissions on applications, especially those that inspire new methodologies.

人工智能

BSNLP 2019

Workshop on  Balto-Slavic Natural Language Processing

全文截稿: 2019-04-26

开会时间: 2019-08-02

会议难度: ★★

CCF分类: 无

会议地点: Florence, Italy

网址：http://bsnlp.cs.helsinki.fi

This Workshop addresses Natural Language Processing (NLP) for the Balto-Slavic languages. The NLP tasks in urgent need of attention include, but are not limited to:

morphological analysis and generation,
morphosyntactic tagging,
syntactic and semantic parsing,
lexical semantics,
named-entity recognition,
text normalisation and processing non-standard language
coreference resolution,
information extraction,
question answering,
information retrieval,
text summarization,
machine translation,
development of linguistic resources.

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