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
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
Kiran Vaidhya
@kiranvaidhya
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.
Fundamentals of supervised learning, convolutional neural networks, cost functions and over-fitting.
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.
https://speakerdeck.com/kvrd18/unsupervised-and-semi-supervised-deep-learning-for-medical-imaging
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
Hosted by
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