posted by organizer: PAPIs || 2722 views || tracked by 3 users: [display]

PAPIs 2017 : 4th International Conference on Predictive Applications and APIs

FacebookTwitterLinkedInGoogle

Link: http://cfp.papis.io/events/2017
 
When Oct 24, 2017 - Oct 25, 2017
Where Boston
Submission Deadline Jul 9, 2017
Notification Due Jul 9, 2017
Final Version Due Jul 9, 2017
Categories    predictive analytics   machine learning   deep learning   apis
 

Call For Papers

The 4th International Conference on Predictive Applications and APIs, 24-25 October in Boston USA.

The Call for Proposals has now opened for PAPIs '17, and we hope you will join us! PAPIs is a series of independent community conferences, dedicated to the application of machine learning to real-world problems to create intelligent applications.

Speakers are a mix from industry and academia, and the audience is a mix of developers, software engineers, data scientists, machine learning engineers, researchers, decision makers, managers, strategists and innovators.

We're also encouraging more formal submissions of industrial experience reports, research papers and review papers (in the form of extended abstracts, short or long papers). They highly contribute to the quality of the conference, and those that are accepted will be published in Proceedings of Machine Learning Research.

This year we are specifically aiming to increase the number of female speakers from 23% last year to a target of 33%, so please share the CfP with women in your network! Applications for funding to reimburse childcare and travel costs will be invited from women and primary carers whose proposals are accepted.

Submissions are now invited for non-commercial talks of a technical or business nature as well as hands-on tutorials or demonstrations of tools and use cases in machine learning. In particular, we are seeking dynamic and focused talks of 20 minutes plus 5 minutes for Q&A, covering one of the following:

Topics of Interest
Innovative machine learning use cases
Challenges and lessons learned integrating machine learning into applications / processes / businesses and new areas; including technical and domain specific challenges or those related to fairness, accountability, transparency and privacy
Techniques, architectures, infrastructures, pipelines, frameworks and API design to create better predictive and intelligent applications (from embedded to web scale)
Tools to democratize machine learning and make building products easier
Needs, trends and opportunities in this space
Tutorials teaching a specific and valuable skill

We welcome practical presentations from beginner-friendly how-tos to cautionary tales and deep dives for experienced professionals. We're looking for a diverse and creative line-up!

Submissions will be accepted until July 9, 2017 at 06:00 GMT.

Submission requirements and key dates for review can be found at http://cfp.papis.io/events/2017.

The official conference website is http://www.papis.io/2017. Here you will find details of keynote speakers, conference registration and the Call for Sponsors.

We look forward to your proposal and hope to see you at PAPIs '17 in Boston at Microsoft N.E.R.D. on 24-25 October 2017.

Related Resources

IEEE Workshop Big Data 2019   IEEE Big Data 2019: Big Data Predictive Maintenance Using Artificial Intelligence
ICML 2020   37th International Conference on Machine Learning
MLPMEA 2019   Special Session On Machine Learning for Predictive Models in Engineering Applications, ICMLA 2019
IWUAS 2020   2020 International Workshop on Unmanned Aircraft Systems (IWUAS 2020)
ICPP 2020   International Conference on Parallel Processing
SDM 2020   SIAM International Conference on Data Mining
AI Applications for A Smart Cyber 2020   Artificial Intelligence Applications for A Smart Cyber Ecosystem: Smart Cities, Cybersecurity and IoT
ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
IoT Stream 2019   IoT Stream 2019: IoT Stream for Data Driven Predictive Maintenance
CVPR 2020   Computer Vision and Pattern Recognition