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 firstname.lastname@example.org or call +91-7676332020.
Object Detection using deep convolutional network
The talk aims to cover the advancements in object detection framework based on convolutional networks. The goal of object category detection is to identify and localize objects of a given type in an image. The main challenge associated with object detection in unconstrained images is to localize the object regardless of its location and scale. I will cover various state of the art methods employed in generic object detection starting from rcnn that employs a third party generic object proposals(selective search) to faster-rcnn which generates the proposals from the convolutional network itself with substantial reduction in running time. I will then cover the role of context for object detection in domains like fashion where region based methods fall short of predicting the correct category and methods to incorporate the context in existing object detection systems.
1. Object detection framework with convolutional network
2. Region based object detection algorithm (R-cnn)
3. Methods to speed up the training and testing time
4. Bypassing the generic object proposal phase(faster-rcnn)
5. Context(surrounding of region) Importance for object detection in fashion domain
6. Methods to incorporate context
Koustubh Sinhal is the founder and CTO of iLenze.com, an AI platform focussed on visual search. He completed his bachelor and Masters degree from IIT Kanpur. He has a long standing interest in Machine learning, Artificial intelligence and computer vision. His novel work at iLenze is currently patent pending and opens door for next generation AI which works for all kind of real-world-images with huge clutter and noise.