Anthill Inside 2017

On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.

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Submissions are closed for this project

MLR Whitefield

About AnthillInside:

In 2016, The Fifth Elephant branched into a separate conference on Deep Learning. Anthill Inside is the new avataar of the Deep Learning conference.
Anthill Inside attempts to bridge the gap bringing theoretical advances closer to functioning reality. Proposals are invited for full length talks, crisp talks and poster/demo sessions in the area of ML+DL. The talks need to focus on the techniques used, and may be presented independent of the domain wherein they are applied.
We also invite talks on novel applications of ML+DL, and methods of realising the same in hardware/software.
Case studies of how DL and ML have been applied in different domains will continue to be discussed at The Fifth Elephant.

https://anthillinside.in/2017/

Topics: we are looking for talks covering the following:

  • Machine Learning with end-to-end application
  • Deep Learning
  • Artificial Intelligence
  • Hardware / software implementations of advanced Machine Learning and Deep Learning
  • IoT and Deep Learning
  • Operations research and Machine Learning

Format:

Anthill Inside is a two-track conference:

  • Talks in the main auditorium and hall 2.
  • Birds of Feather (BOF) sessions in expo area.

We are inviting proposals for:

  • Full-length 40-minute talks.
  • Crisp 15-minute how-to talks or introduction to a new technology.
  • Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
  • Hands-on workshop sessions of 3 and 6 hour duration where participants follow instructors on their laptops.
  • Birds of Feather (BOF) sessions.

You must submit the following details along with your proposal, or within 10 days of submission:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant or last year at Deep Learning.
  3. If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.

Selection Process:

  1. Proposals will be filtered and shortlisted by an Editorial Panel.
  2. Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
  3. Proposers are also encouraged to vote and comment on other proposals submitted here.

We expect you to submit an outline of your proposed talk, either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal to start evaluating your proposal.

Selection Process Flowchart

You can check back on this page for the status of your proposal. We will notify you if we either move your proposal to the next round or if we reject it. Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

A speaker is NOT confirmed a slot unless we explicitly mention so in an email or over any other medium of communication.

There is only one speaker per session. Entry is free for selected speakers.

We might contact you to ask if you’d like to repost your content on the official conference blog.

Travel Grants:

Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.

Commitment to Open Source:

We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.

Important Dates:

  • Deadline for submitting proposals: July 10
  • First draft of the coference schedule: July 15
  • Tutorial and workshop announcements: June 30
  • Final conference schedule: July 20
  • Conference date: July 30

Contact:

For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

Please note, we will not evaluate proposals that do not have a slide deck and a video in them.

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

Arthi Venkataraman

@arthi

Deep learning for feature extraction from incident data

Submitted Apr 12, 2017

Lots of incident data is available in large corporate. However it is Noisy and inaccurate. Classification directly using TFIDF vectorization and machine learning models gives low accuracy. Lots of effort is spent in hand curation of data. Objective is to automatically extract features using deep learning techniques to get a higher lever representation of the text in the incidents. Downstream tasks like classification and clustering will be performed on this space. Presentation will cover the deep learning network architecture of such a system and the results obtained.

Outline

 Problem statement

 Objective

 Tasks
 Overview of Relevant Reference Literature
 What are we trying to do?
 Prior work
 View of data
 Challenge
 Proposed approach
 Network architecture
 Results
 Future work
 Conclusions

• What is Representation Learning? 1 min
• Representation Learning in NLP and it’s practical applications? 2 mins
• Introduction to specific problem we are trying to solve – 1 min
• Deep Learning Architecture - 4 mins
o Our NLP pre-processing pipeline
o Deep Learning network architecture we have used and Parameters trained with
• Our Results , Conclusions and Further work – Rest of time

• OUTCOMES/CONCLUSION
The participants will be exposed to the concept of Representation learning. They will understand the application of Representation learning to the feature extraction space. They will understand how to apply deep learning techniques for natural language processing. Specifically they will be exposed to the deep learning network architecture used, applicability of deep learning in the field of natural language processing, the algorithm used, how the weights are learnt, lessons learnt and results obtained. The unique contribution of this talk is the methodology of application of deep learning for automatically extracting features from the textual incident management domain data.

Requirements

None

Speaker bio

Arthi Venkataraman has 20+ years of experience in the design, development and testing of projects in different domains. She is currently a Senior Member in the Distinguished Members of Technical Staff cadre at Wipro Technologies. Her current role involves development of solutions which involve application of deep learning techniques to natural language processing with intent to better perform in different tasks like classification, clustering, question answering, summarization, etc.
Previously she has been involved in Bot development for different business problems spanning the area of Natural Language Processing, Machine Learning and Semantics Technologies She has a B.E Degree in Computer Science from University Visvesvariah College of Engineering, Bangalore and an MBA (PGDSM) from IIM, Bangalore.
She has previously presented papers and spoken at other international conferences with maximum audience sizes of many hundreds. This presentation is based on Arthi’s experience in feature extraction from natural language using deep learning techniques.

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Learning representations of text for NLP

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