Anthill Inside 2017
On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
##About AnthillInside:
In 2016, The Fifth Elephant branched into a separate conference on Deep Learning. Anthill Inside is the new avataar of the Deep Learning conference.
Anthill Inside attempts to bridge the gap bringing theoretical advances closer to functioning reality.
Proposals are invited for full length talks, crisp talks and poster/demo sessions in the area of ML+DL. The talks need to focus on the techniques used, and may be presented independent of the domain wherein they are applied.
We also invite talks on novel applications of ML+DL, and methods of realising the same in hardware/software.
Case studies of how DL and ML have been applied in different domains will continue to be discussed at The Fifth Elephant.
https://anthillinside.in/2017/
##Format:
Anthill Inside is a two-track conference:
We are inviting proposals for:
You must submit the following details along with your proposal, or within 10 days of submission:
##Selection Process:
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 to start evaluating your proposal.
You can check back on this page for the status of your proposal. We will notify you if we either move your proposal to the next round or if we reject it. Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
A speaker is NOT confirmed a slot unless we explicitly mention so in an email or over any other medium of communication.
There is only one speaker per session. Entry is free for selected speakers.
We might contact you to ask if you’d like to repost your content on the official conference blog.
##Travel Grants:
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
##Commitment to Open Source:
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for 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), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
##Important Dates:
##Contact:
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Please note, we will not evaluate proposals that do not have a slide deck and a video in them.
Hosted by
Abhijeet Katte
@abhik24
Submitted Jun 10, 2017
Most deep learning models are often viewed as deterministic functions, seen as opaque and different from probabilistic models. But that is not fully true. The probabilistic view of machine learning offers confidence bounds for data analysis and decision making, information that a biologist for example would rely
on to analyse her data, or an autonomous car would use to decide whether to take a turn or brake.
In analysing data or making decisions, it is often necessary to be able to tell
whether a model is certain about its output, being able to ask “maybe I need to use more
diverse data? or change the model? or perhaps be careful when making a decision?”.
This talk will be an introduction to application of Bayesianism in understanding uncertainty in modern deep learning. The key takeaways from the crisp talk will be :
What Do We Mean By Being Bayesian ?
Achievements of Modern Deep Learning
Unanswered questions
Why Care About Uncertainty
When Is The Probabilistic Approach Essential?
Bayesian Machine Learning
A Few Example and Results
Applications
Basic understanding of learning and inference phases of (deep) machine learning. Basic understanding of Bayes’ Rule.
I am Abhijeet Katte. I am a Data Scientist at Hands Free Networks, building intelligent systems for automatically handling support for interconnected and IOT devices. I graduated from Dr. Babasaheb Ambedkar Technological University in 2016 with a degree in computer engineering. Before coming to HFN, I was a part of Data Sciene Team at Locus.sh, a logistics tech company and worked as a research assistant at Indian Institute of Science.
I am also interested in computational neuroscience and philosophy of language and the mind.
Links : https://abhik24.github.io
Linkedin: https://linkedin.com/in/abhikatte
https://docs.google.com/presentation/d/1iSGd9TJVZinOnjgNLSJ_WGShGPDCsJ7WB1NHT_aKsQ8/edit?usp=sharing
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
Hosted by
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