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.
Topics: we are looking for talks covering the following:
- Machine Learning with end-to-end application
- Deep Learning
- Artificial Intelligence
- Hardware / software implementations of advanced Machine Learning and Deep Learning
- IoT and Deep Learning
- Operations research and Machine Learning
Anthill Inside is a two-track conference:
- Talks in the main auditorium and hall 2.
- Birds of Feather (BOF) sessions in expo area.
We are inviting proposals for:
- Full-length 40-minute talks.
- Crisp 15-minute how-to talks or introduction to a new technology.
- Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on workshop sessions of 3 and 6 hour duration where participants follow instructors on their laptops.
- Birds of Feather (BOF) sessions.
You must submit the following details along with your proposal, or within 10 days of submission:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant or last year at Deep Learning.
- If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.
- Proposals will be filtered and shortlisted by an Editorial Panel.
- Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
- Proposers are also encouraged to vote and comment on other proposals submitted here.
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.
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”.
- Deadline for submitting proposals: July 10
- First draft of the coference schedule: July 15
- Tutorial and workshop announcements: June 30
- Final conference schedule: July 20
- Conference date: July 30
For more information about speaking proposals, tickets and sponsorships, contact email@example.com or call +91-7676332020.
Please note, we will not evaluate proposals that do not have a slide deck and a video in them.
The Importance of Knowing What We Don’t Know - Bayesianism and Deep Learning
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 :
1. Appreciation and understanding of Bayesianism in general
2. Glimpse of how Bayesianism will influence deep learning in the future
3. Application of Bayesianism for better machine learning models
What Do We Mean By Being Bayesian ?
Achievements of Modern Deep Learning
Why Care About Uncertainty
When Is The Probabilistic Approach Essential?
Bayesian Machine Learning
A Few Example and Results
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.
- Yarin Gal’s Talk - http://mlg.eng.cam.ac.uk/yarin/PDFs/2015_UCL_Bayesian_Deep_Learning_talk.pdf
- Yarin Gal’s PhD Thesis - http://mlg.eng.cam.ac.uk/yarin/index.html
- A history of Bayesian neural networks - http://bayesiandeeplearning.org/slides/nips16bayesdeep.pdf