Deep Learning is a new area of research that is getting us closer in achieving one of the primary objectives of Machine Learning – Artificial Intelligence.
It is used widely in the fields of Image Recognition, Natural Language Processing (NLP) and Video Classification.
Deep Learning Conf is a single day conference followed by workshops on the second day. The conference will have full, crisp and lightning talks from morning to evening. The workshops on the next day will introduce participants to neural networks followed by two tracks of three-hour workshops on NLP and Computer Vision / AI. Participants can join either one of the two workshop tracks.
We are looking for talks and workshops from academics and practitioners of Deep Learning on the following topics:
- Applications of Deep Learning in software.
- Applications of Deep Learning in hardware.
- Conceptual talks and cutting edge research on Deep Learning.
- Building businesses with Deep Learning at the core.
We are inviting proposals for:
- Full-length 40 minute talks.
- Crisp 15-minute talks.
- Lightning talks of 5 mins duration.
Proposals will be filtered and shortlisted by an Editorial Panel. Along with your proposal, you must share the following details:
- Links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience.
- Blog posts you may have written related to your proposal.
- Outline of your proposed talk – either in the form of a mind map or a text document or draft slides.
If your proposal involves speaking about a library / tool / software that you intend to open source in future, the proposal will be considered only when the library / tool / software in question is made open source.
We will notify you about the status of your proposal within two-three weeks of submission.
Selected speakers have to participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you prepare for speaking at the conference.
There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).
Commitment to open source
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like 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), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
Key dates and deadlines
- Proposal submission deadline: 31 May 2016
- Schedule announcement: 15 June 2016
- Conference dates: 1 July 2016
CMR Institute of Technology, Bangalore
For more information about speaking proposals, tickets and sponsorships, contact email@example.com or call +91-7676332020.
Challenges & Implications of Deep Learning in Healthcare
Deep Learning has made leaps and bounds in several industries around us – products ranging from self-driving cars, voice assistants, fashion recognition engines and enterprise bots are no longer science fiction ideas. Despite the advances in several industries, intelligence in healthcare has seen limited penetration. Other than the giants of IBM, few have taken up building intelligent healthcare enterprise solutions.
Applications of deep learning in healthcare covers a broad range of problems ranging from cancer screening and disease monitoring to personalized treatment suggestions. Various sources of data today - radiological imaging (X-Ray, CT and MRI scans), pathology imaging and recently, genomic sequences have brought an immense amount of data at the physician’s disposal. However, we are still short of tools to convert all this data to useful information. This talk aims to demystify some of the challenges, milestones and current state-of-art in healthcare intelligence today.
Over the course of the talk, I will also cover our solution to Multiple Sclerosis lesion segmentation from Brain MRI, which was awarded for best performance at the IEEE- International Symposium on Biomedical Imaging 2015, New York. We use a 3D convolutional neural network (CNN) with novel sub-sampling and efficient training implementation using sparse convolutional kernels.
- Quick recap on milestones achieved by deep learning and in the healthcare space
- Open challenges in DL for Medicine – Access to data, number of samples, large sizes, variations based on demographics, need for high accuracy solutions
- Solution to our winning IEEE-ISBI challenge entry on Multiple Sclerosis using 3D CNNs
- Current state of art in research & industry – how far are we from complete diagnosis?
Suthirth Vaidya is a co-founder at Predible Health, which develops AI based recognition tools for medical imaging. His team previously won the Grand Challenge at IEEE-International Symposium on Biomedical Imaging 2015 at New York, USA for application of deep learning for segmentation of multiple sclerosis lesions in the brain. He underwent his undergraduate and master’s degree studies from IIT Madras.
- LinkedIn - www.linkedin.com/in/suthirth
- Predible Health - www.predible.co
- Times of India article - http://timesofindia.indiatimes.com/life-style/health-fitness/health-news/IIT-Madras-teams-research-to-help-detect-multiple-sclerosis/articleshow/48326781.cms