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.
PyTorch Demystified, Why Did I Switch
PyTorch entered into the realm of DL framework with the promise of being “Numpy on GPU”. The obvious failures of static graph implementation for certain use cases is increasing industry wide adoption of PyTorch. Dynamic Computation Graph being the backbone of PyTorch, comes with some perks.
- Super intuitive
- Shallow learning curve
- Amazing community and discussion forum
- Easy debugging
It puts you explicitly in control of your compute, there’s no compiler trying to be clever and “help you” or speed your code up, but in reality mostly leading to a massive amount of debugging headaches. PyTorch should be the go-to framework for the problem with “dynamicity” in the core, especially for RNNs and highly dynamic Reinforcement Learning algorithms.
My talk would be to convince the audience about the advantages of PyTorch. For what problems pytorch would be the best solution. I will have benchmarks with popular DL frameworks that review speed, performance, memory, and easiness. I’ll try to build on top of a comparison foundation with TF and NumPy so that the audience could correlate with their day to day research/development projects. The demo would be to portray the best cases where pytorch really shines over other DL frameworks.
Note: Code I developed for the presentation would be available under MIT licence
- Computational Graphs [5 minutes]
- Static and Dynamic graphs [5 minutes]
- Introduction to PyTorch [10 minutes]
- nn Module
- Examples [10 minutes]
- Simple Neural Networks
- Encoder Decoder Network
- Stack-augmented Parser-Interpreter Neural Network
- PyTorch and Caffe2 [2 minutes]
- How to visualize [5 minutes]
- Benchmarking [1 minute]
- Basic knowledge about how Neural Networks work
- Knowledge on Numpy operations is recommended
- It would help (but not mandatory) if the audience could go through this 60 minutes pytorch tutorial
I am working as an AI developer in CoWrks. Here we work on a broad spectrum of use-cases in NLP and CV. I am currently focusing on NLP, particularly word representation using image, speech and predefined lexical structure. In internet, I go by hhsecond.