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 firstname.lastname@example.org or call +91-7676332020.
Please note, we will not evaluate proposals that do not have a slide deck and a video in them.
Deep learning based OCR engine for the Indus script
Computational epigraphy is an interdisciplinary area that combines computing and the study of ancient inscriptions. The main challenge or bottleneck faced in the field of epigraphical research is the lack of standardized corpora of the ancient scripts under study. Preparing such data from raw archaeological records, requires laborious human effort, expertise and a lot of time. Machine Learning has been used in the past to reduce human effort in epigraphical research, in problems such as classification and search for graphemic patterns. However, ML and in specific Deep Learning has not been applied yet, for the complementary task of corpus preparation. This talk will be focusing on how a deep learned pipeline architecture was designed to serve as an OCR (Optical Character Recognition) engine that is capable of reading the Indus script, one of the very ancient and undeciphered inscriptions of the Harappan Civilization. This pipeline takes as input, images of the undeciphered Indus script, as found in archaeological artifacts, and returns as output a string of graphemes, suitable for inclusion in a standard corpus.
This can be extended to a Full Talk too, based on the response.
- What is computational epigraphy?
- The long undeciphered Indus script
- Why still undeciphered?
- ML in the study of Indus scripts
- Need for corpus formulation
- Why deep learning?
- The design decisions in DL
- Deep features
- Transfer learning and fine tuning
- Data augmentation
- The design decisions in DL
- The deep learned pipeline architecture
- Region proposal
- Text region formulation
- Symbol segmentation
- Symbol identification
- Evaluating the pipeline’s performance
- Limitations of the pipeline
- Generalizing this architecture to other ancient inscriptions
This is Satish Palaniappan, a CS graduate from SSN CE (Sri Sivasubramaniya Nadar College of Engineering). I am currently working as a software engineer at Qube Cinema Technologies. Alongside this, I am also working as a Research Assistant under Prof. Ronojoy Adhikari at the IMSc (Institute of Mathematical Sciences), Chennai.
My work domains:
Deep Learning, Machine Learning, Computer Vision, NLP, Algorithm Design
Work at IMSc:
Currently, we are developing a deep-learned pipeline architecture that enables computers to read ancient inscriptions from images of archaeological artifacts. The working prototype of the same applied to the Indus script of the Harappan civilization and our paper titled “Deep Learning the Indus Script” arXived at: arXiv:1702.00523v1 created a buzz among the research community and in the media alike. The fully functional Indus script OCR engine will be open sourced and be available as an API based service, soon.
Work at Qube Cinemas:
- Deep learning based computer vision algorithm for mining the viewer demographics.
- Real-time adaptive selection and resource allocation algorithm for businesses.
- Research paper “Deep Learning the Indus Script”: https://arxiv.org/abs/1702.00523 (paper in arXiv, to be submitted to PLoS One)
- Related slide deck: https://rawgit.com/tpsatish95/OCR-on-Indus-Seals/master/slides/index.html#1 (Prepared during initial stages of our research)
- Related code: https://github.com/tpsatish95/OCR-on-Indus-Seals
- Press coverage:
- The Hindu: http://www.thehindu.com/sci-tech/science/chennai-team-taps-ai-to-read-indus-script/article17448690.ece
- The Verge: http://www.theverge.com/2017/1/25/14371450/indus-valley-civilization-ancient-seals-symbols-language-algorithms-ai#EQQA6r
- Times of India: http://timesofindia.indiatimes.com/city/chennai/app-may-help-decipher-indus-valley-symbols/articleshow/57281369.cms
- SBS Radio, Australia: http://www.sbs.com.au/yourlanguage/tamil/en/content/app-decipher-ancient-symbols?language=en