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.
Making Deep Neural Networks smaller and faster
Deep neural networks with millions of parameters are at the heart of many state of the art machine learning models today. However, is has been shown that models with much smaller number of parameters can also perform just as well. A smaller model has the advantage of being faster to evaluate and easier to store - both of which are crucial for real-time and embedded / mobile applications. In this talk, I intend to provide a brief overview of such model compression techniques. Using these techniques, it is possible to compress neural networks by as much as 10x and speed up inference by 3-4x.
First, I shall motivate the general problem of model compression and it’s relevance for real-world applications.
Then, I shall provide overviews of the following papers:
1) Learning both Weights and Connections for Efficient Neural Networks, NIPS 2015
2) Deep Compression, ICLR 2016
3) Learning the Architecture of Deep Neural Networks, arxiv 2016
Familiarity with Convolutional Neural Networks
Suraj is a second year Master’s student at Video Analytics Lab, Indian Institute of Science. From the past one and a half years, he has been working on the problem of model compression. His work has been presented previously at British Machine Vision Conference (BMVC) - 2015.