Anthill Inside 2017

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

DATE

29 July 2017, Bangalore

STATUS

Open for feedback


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.


Confirmed sessions

Deep Reinforcement Learning : A tutorial

Vikas Raykar (@vikasraykar) (proposing)

  • Full talk
  • Beginner
  • 1 upvotes
  • 0 comments
  • Tue, 25 Jul

OTR - DL and image

Sandhya Ramesh (@sandhyaramesh) (proposing)

  • Full talk
  • Beginner
  • 1 upvotes
  • 0 comments
  • Sat, 22 Jul

Apache MXNet, a highly memory efficient deep learning framework

Girish Patil (@bookworm)

  • Crisp talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Sat, 22 Jul
  • slideshow

AI in self driving vehicles - a practitioner's perspective

Saad Nasser (@sdnssr)

  • Full talk
  • Beginner
  • 1 upvotes
  • 0 comments
  • Sat, 22 Jul

AI on IA

Mukesh Gangadhar (@mukgbv)

  • Workshop
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Tue, 18 Jul

AI: Unleashing the next wave

Rebanta Dutta (@anthillevent) (proposing)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Mon, 17 Jul

Panel on product and AI

Vijay Gabale (@vijaygabale)

  • Birds of a Feather (BOF) session
  • Beginner
  • 4 upvotes
  • 0 comments
  • Fri, 14 Jul

Keep Calm and Trust your Model - On Explainability of Machine Learning Models

Praveen Sridhar (@psbots)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Mon, 10 Jul
  • play_arrow
  • slideshow

Identifying Urban Makeshift Communities using satellite imagery and geo-coded data

Akarsh Zingade (@akarsh-zingade)

  • Crisp talk
  • Intermediate
  • 18 upvotes
  • 2 comments
  • Mon, 10 Jul
  • play_arrow
  • slideshow

How Deep is Deep Learning?

Amar Lalwani (@amar1707)

  • Crisp talk
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Sun, 9 Jul
  • play_arrow
  • slideshow

Practical Deep Learning

saurabh agarwal (@saurabh-agl) (proposing)

  • Workshop
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Thu, 22 Jun

Adversarial attacks on deep learning models

Konda Reddy Mopuri (@mkreddy)

  • Full talk
  • Intermediate
  • 3 upvotes
  • 1 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

Synthetic Gradients – Decoupling Layers of a Neural Nets

Anuj Gupta (@anujgupta82)

  • Full talk
  • Intermediate
  • 6 upvotes
  • 0 comments
  • Fri, 9 Jun
  • slideshow

PyTorch Demystified, Why Did I Switch

Sherin Thomas (@hhsecond)

  • Full talk
  • Beginner
  • 8 upvotes
  • 3 comments
  • Sun, 28 May
  • play_arrow
  • slideshow

Hitchhiker’s Guide to Generative Adversarial Networks (GANs)

Ramanan Balakrishnan (@ramananbalakrishnan)

  • Full talk
  • Intermediate
  • 14 upvotes
  • 4 comments
  • Sat, 29 Apr
  • slideshow

Unsupervised and Semi-Supervised Deep Learning for Medical Imaging

Kiran Vaidhya (@kiranvaidhya)

  • Full talk
  • Advanced
  • 15 upvotes
  • 0 comments
  • Sat, 29 Apr
  • play_arrow
  • slideshow

Learning representations of text for NLP

Anuj Gupta (@anujgupta82)

  • Workshop
  • Intermediate
  • 27 upvotes
  • 4 comments
  • Thu, 20 Apr
  • slideshow

Saving the Princess with Deep Learning

Navin (@navinpai)

  • Crisp talk
  • Intermediate
  • 3 upvotes
  • 1 comments
  • Mon, 10 Apr
  • play_arrow
  • slideshow

Unconfirmed proposals

Deep Learning approaches for Named Entity Recognition

Vijay Ramakrishnan (@vijay120)

  • Crisp talk
  • Intermediate
  • 1 upvotes
  • 2 comments
  • Thu, 13 Jul
  • play_arrow
  • slideshow

Getting Started with GPU Accelerated Deep Learning

Gaurav Goswami (@gauravgoswami)

  • Crisp talk
  • Beginner
  • 1 upvotes
  • 2 comments
  • Mon, 10 Jul
  • slideshow

Deep learning with limited data

Aman Neelapa (@aman42)

  • Crisp talk
  • Intermediate
  • 6 upvotes
  • 0 comments
  • Wed, 5 Jul

Augmenting Solr’s NLP Capabilities with Deep-Learning Features to Match Images

Kumar Shubham (@kumar-shubham)

  • Crisp talk
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Wed, 28 Jun
  • play_arrow
  • slideshow

CNN for NLP

Malaikannan Sankarasubbu (@malai)

  • Full talk
  • Intermediate
  • 34 upvotes
  • 1 comments
  • Fri, 23 Jun
  • play_arrow
  • slideshow

Leonardo Machine Learning Foundation - Adding Intelligence to your Enterprise Business

sainath v (@sapcloudengineers)

  • Crisp talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Thu, 22 Jun
  • play_arrow
  • slideshow

Application Dependency Data Performance Mapping tool - Dynatrace

Chandrish M (@chandrish)

  • Crisp talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Thu, 22 Jun
  • play_arrow
  • slideshow

Developing agents with Deep Reinforcement learning

Satwik Kansal (@satwikkansal)

  • Full talk
  • Beginner
  • 1 upvotes
  • 2 comments
  • Tue, 20 Jun
  • play_arrow
  • slideshow

Neural Machine Translation

Ashish Mogha (@ashishmogha)

  • Full talk
  • Intermediate
  • 20 upvotes
  • 0 comments
  • Mon, 19 Jun
  • play_arrow
  • slideshow

Making a Text-Summarizer with Keras

Gur Raunaq Singh (@raunaqsoni)

  • Crisp talk
  • Intermediate
  • 10 upvotes
  • 2 comments
  • Mon, 19 Jun

Keras: Deep Learning for Python

Fariz Rahman (@farizrahman4u)

  • Full talk
  • Advanced
  • 9 upvotes
  • 1 comments
  • Fri, 16 Jun

Demystifying Visual Question Answering

Laksh Arora (@techedlaksh)

  • Full talk
  • Intermediate
  • 19 upvotes
  • 1 comments
  • Wed, 14 Jun
  • play_arrow
  • slideshow

From RNN to Attention

Sarath R Nair (@s4sarath)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 3 comments
  • Mon, 12 Jun

Neural Stack: Augmenting Recurrent Neural Networks with Memory

SATYAM SAXENA (@sam89)

  • Full talk
  • Intermediate
  • 3 upvotes
  • 3 comments
  • Sun, 11 Jun
  • slideshow

Decoding Neural Image Captioning

Sachin Kumar (@sachinkmr)

  • Full talk
  • Intermediate
  • 36 upvotes
  • 1 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

Highway Networks and ResNet : A deeper approach towards Deep Learning .

Vasudev Singh (@vasu-dev)

  • Full talk
  • Intermediate
  • 20 upvotes
  • 0 comments
  • Sat, 10 Jun
  • slideshow

Deep Learning Applications: A hands-on approach

Shubham Dokania (@shubhamdokania)

  • Workshop
  • Intermediate
  • 52 upvotes
  • 5 comments
  • Sat, 10 Jun
  • slideshow

The Importance of Knowing What We Don’t Know - Bayesianism and Deep Learning

Abhijeet Katte (@abhik24)

  • Crisp talk
  • Beginner
  • 22 upvotes
  • 2 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

Supervised-machine-learning without coding

Rajesh Gudikoti (@ragudiko)

  • Full talk
  • Beginner
  • 1 upvotes
  • 4 comments
  • Wed, 7 Jun

Object Classification in 3D: Working with LiDAR point clouds

Akbar Ladak (@bluplaneter)

  • Crisp talk
  • Intermediate
  • 4 upvotes
  • 3 comments
  • Wed, 7 Jun
  • play_arrow
  • slideshow

Taming Convolution Neural Networks for Image Recognition

saurabh agarwal (@saurabh-agl)

  • Full talk
  • Intermediate
  • 14 upvotes
  • 0 comments
  • Sun, 30 Apr
  • play_arrow
  • slideshow

Retail Loss Prevention based on Deep Learning

Subramanya Mayya (@smayya)

  • Full talk
  • Intermediate
  • 6 upvotes
  • 1 comments
  • Sun, 30 Apr

Deep Type - deep convolutional neural networks for style transfer in typography

irfan basha sheik (@sheikirfanbasha)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Sun, 30 Apr

Deep learning based OCR engine for the Indus script

Satish Palaniappan (@satishpalaniappan)

  • Crisp talk
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Sat, 29 Apr
  • play_arrow
  • slideshow

Typography detection using Deep Convolutional Neural Networks

Pallavi Ramicetty (@pallaviramicetty)

  • Crisp talk
  • Intermediate
  • 2 upvotes
  • 4 comments
  • Sat, 29 Apr
  • play_arrow
  • slideshow

KERAS: A Versatile Modeling Layer For Deep Learning

Ananth Krishnamoorthy (@akrishnamoorthy)

  • Full talk
  • Intermediate
  • 5 upvotes
  • 0 comments
  • Thu, 27 Apr
  • slideshow

Malware Detection and Pattern Recognition using Deep Learning

Vidyasagar Nallapati (@dumbyoda)

  • Crisp talk
  • Intermediate
  • 1 upvotes
  • 2 comments
  • Thu, 27 Apr
  • slideshow

Information Retrieval using Deep Learning

shashank gupta (@shash273)

  • Full talk
  • Intermediate
  • 9 upvotes
  • 2 comments
  • Wed, 26 Apr
  • slideshow

Deep Learning with TensorFlow

Abdul Muneer (@abdul-muneer)

  • Workshop
  • Intermediate
  • 7 upvotes
  • 0 comments
  • Wed, 26 Apr

Streaming video analytics using deep learning on large scale surveillance data @ Fractal Analytics

abhineet verma (@averma)

  • Full talk for data engineering track
  • Advanced
  • 25 upvotes
  • 2 comments
  • Tue, 25 Apr
  • play_arrow
  • slideshow

Named Entity Recognition using DL methods

haridas n (@haridasn)

  • Full talk
  • Intermediate
  • 7 upvotes
  • 1 comments
  • Mon, 24 Apr

Introduction to Bounding Box Neural Networks

Anil Hebbar (@ahebbar)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 2 comments
  • Mon, 24 Apr
  • play_arrow

Deep learning for feature extraction from incident data

Arthi Venkataraman (@arthi)

  • Crisp talk
  • Intermediate
  • 6 upvotes
  • 4 comments
  • Wed, 12 Apr
  • play_arrow

Understanding Neural Networks with Theano

Jaidev Deshpande (@jaidevd)

  • Workshop
  • Intermediate
  • 2 upvotes
  • 2 comments
  • Mon, 10 Apr
  • slideshow