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

Kiran Vaidhya

@kiranvaidhya

Unsupervised and Semi-Supervised Deep Learning for Medical Imaging

Submitted Apr 29, 2017

Availability of labelled data for supervised learning is a major problem for narrow AI in current day industry. In imaging, the task of semantic segmentation (pixel-level labelling) requires humans to provide strong pixel-level annotations for millions of images and is difficult when compared to the task of generating weak image-level labels. Unsupervised representation learning along with semi-supervised classification is essential when strong annotations are hard to come by.

This talk will introduce you to the techniques available in unsupervised learning and semi-supervised learning with specific focus on brain tumor segmentation from MRI using Stacked De-noising Auto-Encoders (SDAEs), which achieved competitive results in comparison with purely supervised Convolutional Neural Networks (CNNs), and highlight recent breakthroughs in AI for computer vision. Although the focus is on medical imaging, the techniques will be presented in a domain agnostic manner and can be easily translated for other sectors of deep learning.

Outline

Introduction - [10 mins]

  • Deep learning in Medical Imaging
  • Diagnosing Glioblastoma in brain with MRI
  • Annotation problem
  • Pre-processing

Auto-encoders - [15 mins]

  • Auto-encoders
  • Unsupervised learning
  • Pre-training deep autoencoders on unlabelled data
  • Fine-tuning on limited labelled data

Unsupervised learning - [5 mins]

  • Novelty detection using autoencoder
  • Segmentation using unsupervised learning

Results - [5 mins]

  • Post-processing
  • Segmentation results w.r.t state-of-the-art

Conclusions - [5 mins]

  • Unsupervised + Supervised in one go
  • Ladder Networks
  • Y-Nets
  • The future of unsupervised learning

Requirements

Fundamentals of supervised learning, convolutional neural networks, cost functions and over-fitting.

Speaker bio

Kiran Vaidhya holds a dual degree (B.Tech + M.Tech) in Engineering Design (specialization in Biomedical Design) from IIT Madras. He has been heavily involved in Computer Vision and Medical Imaging for the past 4 years. His Master’s thesis was on brain tumor segmentation from MRI using Semi-Supervised Deep Learning. His work has been published and accepted by leading medical imaging journals like MICCAI.

Post his graduation, he joined Predible Health and is currently working as an Algorithms Researcher for CAD (Computer Aided Diagnosis) system design. Deep learning is a natural part of his work in order to derive data-driven insights. He has been actively involved in the development of Torch and has extensive experience with Theano and TensorFlow.

Links

Slides

https://speakerdeck.com/kvrd18/unsupervised-and-semi-supervised-deep-learning-for-medical-imaging

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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