Anthill Inside 2017
On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
##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/
##Format:
Anthill Inside is a two-track conference:
We are inviting proposals for:
You must submit the following details along with your proposal, or within 10 days of submission:
##Selection Process:
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.
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:
##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
shashank gupta
@shash273
Submitted Apr 26, 2017
Neural networks are current state-of-the-art in almost all Computer Vision, Natural Language Processing and Speech tasks. Convolution Neural Networks, a deep learning model are go-to choice in Computer Vision. Similarly Recurrent Neural Networks (RNNs) are popular choice in NLP. The area of information retrieval is no different. Neural nets are slowly progressing towards becoming state-of-the-art in this field too. In this talk, we will discuss how deep learning can be used to design information retrieval systems. We will start with introduction to some key concepts in Neural IR followed by a practical example of Content based article recommendation system using deep learning.
We will cover the following topics :
In the practical example, we will go through how to apply the concepts learned to build a content based article recommendation system.
Shashank is a MS by Resaerch student in Information Retrieval and Extraction lab (search.iiit.ac.in) at IIIT-Hyderabad.
He is working towards the application of Deep Learning in Social Media Analytics and Inforamation Retrieval. He recently published two papers in World Wide Web (WWW) conference held in Perth, Australia. WWW is a very prestigious and top tier conference in the area of Web and Social Media Analytics. His work was on detecting hateful and abusive text from Social media.
His work was awarded the best poster award at the conference.
His WWW work was featured in Times of India and other news outlets too.
Currently he is working on application of Machine learning and Deep Learning in healthcare domain.
His recent publications are:
https://drive.google.com/file/d/0B-7peEFiNjnUUWtqdXNIWkpSNXM/view?usp=sharing
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