Anthill Inside 2017

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

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

saurabh agarwal

@saurabh-agl

Taming Convolution Neural Networks for Image Recognition

Submitted Apr 30, 2017

The talk is about CNN’s the poster-boys of Deep Learning. One of the most successful models which have given absolutely amazing results in image recognition tasks. The talk will first cover the basics of Convnet. Talk about the reasons why the world uses and what makes them great models for Image recognition. On the surface not much has changed in convolutional neural networks, but in last five years there have been considerable advances, in the architectures and training procedures. The talk will go down into the gory details of all these advances and what they mean for people training ConvNets. Then we will get into the practical advice for training ConvNets about the do’s,dont’s and the must.
Pre-requisites- Since we will start from the scratch about ConvNets. The only thing which is expected out of the audience is understanding of Back-Propogation and Stochastic Gradient Descent. Also some linear algebra basics, will also be helpful.

Key-takeaways - A good understanding of ConvNets. How to use them effectively and practical advice on how to train them. Also a lot of Info on latest advances in architecture and training methodologies.

Intended-audience - Practitioners, Researcher’s and Any other inquisitive soul who want’s to know about ConvNets

Outline

Structure of talk:
Where it all Started (1min)
The big-bang of ConvNets (1min)
Structure of Convnets (5min)
How far Convnet’s have came from the big-bang in terms of advances (2min)
Specifics into the latest architectures-
Alexnet- The big-bang (3min)
ZFnet - How visualization helps (5min)
There will be a small tangent here and we will talk some-stuff about visualization using Deconvolution

VGGnet- A bigger deeper version (2min)
Inception-architecture(5min)
Residual Networks(5min)
Practical advice on how to train a Network- both from scratch as well using Transfer Learning(10min)

Speaker bio

Saurabh has been working at MAD Street Den, Chennai as a Machine Learning Engineer since past year and a half,specifically working on Deep Learning based products. He loves to train Convolutional Neural Networks of all types and sizes for different applications. Apart from CNN’s he has special interest in recurrent architectures and discovering their powers. When he is not working on DL based stuff, he loves to play around with micro-controllers.

Links

Slides

https://docs.google.com/presentation/d/1oRRaZjcgKsYnjGDPwLUGDKbOgU8h0Kk6llG77aYuDYA/pub?start=false&loop=false&delayms=3000

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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