The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.
Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.
We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.
We are looking for application level and tool-centric talks and tutorials on the following topics:
- Deep Learning
- Text Mining
- Computer Vision
- Social Network Analysis
- Large-scale Machine Learning (ML)
- Internet of Things (IoT)
- Computational Biology
- ML in healthcare
- ML in education
- ML in energy and ecology
- ML in agriculrure
- Analytics for emerging markets
- ML in e-governance
- ML in smart cities
- ML in defense
The deadline for submitting proposals is 30th April 2016
This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:
- Full-length 40 minute talks.
- Crisp 15-minute talks.
- Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on Workshop sessions, 3 and 6 hour duration.
Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.
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.
We will notify you about the status of your proposal within three weeks of submission.
Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for 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).
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.
- Revised paper submission deadline: 17 June 2016
- Confirmed talks announcement (in batches): 13 June 2016
- Schedule announcement: 30 June 2016
- Conference dates: 28-29 July 2016
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.
For more information about speaking proposals, tickets and sponsorships, contact email@example.com or call +91-7676332020.
Continuous online learning for classification tasks
At Airwoot (now acquired by Freshdesk), we model NLP-based margin-based classifiers to filter spam from relevant customer tweets/post on social media. We work with the language of social, and this introduces a challenge of continuously adapting our models to the change in social verbiage. The language of social is dynamic with new hashtags, acronyms and induced spelling mistakes forcing us to update our models frequently. Moreover, the relevance or the noise is not same for every user (very similar to the idea of relevance in email inbox).
So, we built a per-user statistical model to capture the preferences of users. It seems like its not a trivial problem to solve. This requires us to ensemble the global learning (remember the evolution in language) and local learning (basis on the feedback of the user) to classify the conversation. The local model must be able to capture the notion of
concept drift i.e the temporal (and recent) change in data. In this talk, we will showcase how we are able to do continuous online learning using simple but powerful perceptrons.
- Introduction to the problem of online continuous learning
- Examples of Concept drift
- Capturing of concept drift using perceptron
- Fallacies of a local-global model and the need for ensembling
- Towards a robust ensembling manager
Sit back and learn about a powerful learning technique. We will go under the skin for everyone to understand.
I am an entrepreneur and machine learning researcher. I dropped out of my doctoral program in 2012 and founded Airwoot, a company that help businesses deliver customer support on social using lot of mathematical tricks. Airwoot is now acquired by Freshdesk where I continue to build the technology that can teach machines to learn about natural language and emotions.
I completed my masters from DTU Denmark and was pursuing Ph.D from Hasso-plattner Institute, Berlin. In my academic career I contributed research at Macquarie University, Infocomm Research Singapore and the Swedish Institute of Computer Science.