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.
Deep Dive Into Building Chat-bots Using Deep Learning
There has been growing interest on shedding boring and cumbersome “search and get thousand results” interface to move towards a “conversational” interface to ease the reception of deluge of information in various web and mobile applications. While a naive search bar that simplified information extraction and delivery of web pages was the rage in early 2000s, exponential increase in data and information on the Internet is making it natural for the search bar to evolve into a smarter and responsive interface. Not only can this search bar be more responsive in terms of guiding users in an iterative aka conversation based search, but it can also be assistive in fetching information that suits the interests of different users. Sooner or later, such conversational interfac will replace the old-generation search bar.
However, building such conversational interface has several technology challenges: a) the interface has to understand and extract intent from natural human inputs in terms of text or speech (this is partly done by the current generation search interfaces), b) it has to generate meaningful dialogue responses to engage users into a conversation, c) it has to understand and use context to assist users in fetching the most relevant information, and while doing so d) make the information results personalized to the user as she uses the interface over time.
How do we solve these technology challenges? Where are we in terms of building systems and solutions to tackle them? Is the ‘conversational search’ a hype or soon to be reality? How are recent advances in deep learning playing their role towards building such ‘chat bots’? In this talk, I will take a deep dive to answer these questions and present the state of the art in building conversational agents using deep learning.
Specifically, I will talk about advances in deep text mining that make it possible to extract intent from text: text segmentation or sequence labeling to extract entities from a sentence or paragraph. I will also talk about, how can one build a dialogue generation system (especially in the absence of a large corpus of dialogues) using information on the web. I will then give an example of a chat bot that uses context in a conversation to recommend apparels and fashion products to consumers, and assists them to arrive at the product that they would like to buy in no time.
Overall, the talk will give an overview of the current state of deep learning techniques to build a chat bot, and provide details a few techniques such as LSTM or sequence to sequence learning in bulding a chat bot in reality.
Motivation: exponential growth in information, limitations of current search interfaces, consumer expectations
Components of building a chat bot: intent extraction, dialogue generation, meaningful search, assistance based conversation
Deep Learning techniques: Text segmentation, Sequence labeling, sentence generation, conversation generation
Demo and example: A chat bot in fashion commerce domain
Vijay Gabale is co-founder and CTO of Infilect, an AI-powered Commerce Platform. Infilect has been building a fashion commerce platform to provide exceptional shopping experiences to the Internet consumers. The company has made several innovations in deep learning to process rich multi-media data (text, image, videos) to improve discovery, search and personalization experiences of online consumers.
Prior to co-founding Infilect, he was a research scientist with IBM Research Labs. He graduated with a PhD from IIT Bombay, India in 2012. He has several top tier research publications and software patents to his name. He is also co-organizer of ‘Deep Learning Bangalore’ meetup. He has been actively working in deep learning for past several years and has give several talks in and outside India on the research and applications of deep learning in e-commerce.
- Recent talks:
- ACM IKDD 2016: Experience Driven Commerce Using Deep Learning http://ikdd.acm.org/Site/CoDS2016/schedule.html (Attendance 100+)
- IEEE COMSNETS 2016: Matching Fashion Blogs to Fashion Commerce http://www.comsnets.org/archive/2016/waci_workshop.html (Attendance 80+)
- IBM Research, I-CARE 2015: Contextual Product Discovery in The Wild https://university-relations.in/wps/portal/icare2013 (Attendance 60+)