Proposals
Anthill Inside 2019

Anthill Inside 2019

A conference on AI and Deep Learning

Anthill Inside

India’s community of deep learning and artificial intelligence practitioners.

Propose a session

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2019

Taj M G Road, Bangalore, Bangalore

Call for proposals

Format:

Anthill Inside 2019 is a single track conference. Birds of Feather (BOF) sessions, round table discussions and office hours with speakers will be held in parallel with talks in the main auditorium.

We are accepting proposals for:

  1. Full-length (40 min) and crisp (20 min) talks.
  2. Birds of Feather (BOF) sessions – of 1 hour duration – on focussed topics.
  3. Tutorials, explaining core concepts in DL, ML and AI. Tutorials are of 1.5 hours to 2 hours duration.
  4. Hands-on workshops on Machine Learning, statistics, modelling, deep learning, NLP and Computer Vision.

Audience at Anthill Inside:

  1. Data scientists
  2. Senior AI engineers
  3. Architects
  4. Product managers
  5. Product engineers
  6. Founders and key decision makers from startups, mid-sized organizations and enterprises who are solving problems/facing challenges around running/managing infrastructure for AI and ML.
  7. Providers of managed services such as AWS, Google Cloud and Azure.
  8. GPU and CPU solution providers such as NVidia, Intel and others.

Topics for submitting talks, workshops, tutorials and BOF sessions:

  1. Practical application of concepts, including:
    1.1 Bayesian Networks
    1.2 Reinforcement learning
    1.3 Knowledge Graphs
    1.4 Interpretability

  2. Data storage case studies: we are specifically interested in hearing about:
    2.1 What do you do with data that has lost its currency?
    2.2. How do you deal with privacy issues for vast amounts of irrelevant data?

  3. Tools for AI, ML and Deep Learning. Here, we want to hear about:
    3.1 Whether you use third-party tools? If yes, why?
    3.2 Do you adopt and retrofit existing tools? Again, why? Give us a detailed case study.
    3.3 Do you develop tools for AI/ML/DL in-house? Why are in-house tools necessary for your case?

  4. Storing data on the cloud and cloud strategy.
    4.1 Do you have a multi-cloud strategy? Share this with the community.
    4.2 How do you deal with lock-in situations with single providers?

  5. GPU versus CPU – when you do use either and why? How do the strengths and limitations of each play out for your use case?

  6. Case studies of practical applications of Computer Vision, NLP and Deep Learning to solve business problems.

Anthill Inside’s speaking policies:

We only accept one speaker per talk. This is non-negotiable. Workshops or tutorials may have more than one instructor.

Selection criteria:

The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:

  1. If the technology or solution is proprietary, and you want to speak about your propritary solution to make a pitch to the audience, you should pick up sponsored session. This involves paying for the speaking slot. Write to anthillinside.editorial@hasgeek.com for details.
  2. If your solution is closed source, you should consider proposing a talk explaining why you built it in the first place; what options did you consider (business-wise and technology-wise) before making the decision to develop the solution; or, what is your specific use case that left you without existing options and necessitated creating the in-house solution.

The criteria for selecting proposals, in the order of importance, are:

  1. Key insight or takeaway: what can you share with participants that will help them in their work?
  2. Structure of the talk and flow of content: a detailed outline – either as mindmap or draft slides or textual decription – will help us understand the focus of the talk, and the clarity of your thought process.
  3. Ability to communicate succinctly, and how you engage with the audience. You must submit link to a two-minute preview video explaining what your talk is about, and what is the key takeaway for the audience.

How to submit a proposal (and increase your chances of getting selected):

The following guidelines will help you in submitting a proposal:

  1. Focus on why, not how. Explain to participants why you made a business or engineering decision, or why you chose a particular approach to solving your problem.
  2. The journey is more important than the solution you may want to explain. We are interested in the journey, not the outcome alone.
  3. Show what participants from other domains can learn/abstract from your journey/solution.
  4. We do not accept how-to talks unless they demonstrate latest technology. If you are demonstrating new tech, show enough to motivate participants to explore the technology later.
  5. Similarly, we don’t accept talks on topics that have already been covered in the previous editions.

Passes and honorarium for speakers:

We pay an honararium of Rs. 3,000 to each speaker. Confirmed speakers also get a pass to the conference. We do not provide free passes for speakers’ colleagues and spouses. Please don’t ask us for this.

Travel grants for outstation speakers:

Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. Anthill Inside is funded through ticket purchases and sponsorships; travel grant budgets vary.

Last date for submitting proposals is 15 September.

Contact details:

For information about speaking or teaching workshops at Anthill Inside, write to anthillinside.editorial@hasgeek.com or call +91 7676332020

Propose a session

All proposals

Confirmed sessions

Document digitization - Rethinking it with Deep Learning

Nischal HP (@nischalhp)

  • 1 comments
  • Mon, 26 Aug

Feast: Feature Store for Machine Learning

Willem Pienaar

  • 0 comments
  • Tue, 20 Aug

Building Products with ML: A Workshop for Product & Engg Managers

lavanya TS (@lavanyats)

  • 0 comments
  • Tue, 20 Aug

Demystifying deep reinforcement learning

Uma Sawant (@umasawant)

  • 0 comments
  • Thu, 15 Aug

Myths and Realities of Data Labeling for Deep Learning

Vijay Gabale

  • 0 comments
  • Thu, 15 Aug

Birds of a Feather on Interpretability

Jacob Joseph (@jacjose) via Abhishek Balaji (@booleanbalaji)

  • 0 comments
  • Tue, 13 Aug

NLP bootcamp

Anuj Gupta (@anuj-gupta)

  • 0 comments
  • Wed, 15 May

Attention based sequence to sequence models for natural language processing

Madhu Gopinathan (@mg123)

  • 1 comments
  • Fri, 26 Apr

Unconfirmed proposals

Building a Recommendation Engine for diverse content and user behaviors

Priyanshu Chandra (@priyanshu-chandra)

  • 0 comments
  • Sun, 8 Sep

Simplifying Distributed Deep Learning in Cloud

Rahul Ghosh

  • 0 comments
  • Sat, 7 Sep

Probabilistic Modeling – a tutorial on Bayesian Networks

Ashish Kulkarni (@kulashish)

  • 0 comments
  • Sat, 7 Sep

Hangar; git for your data

Sherin Thomas (@hhsecond)

  • 0 comments
  • Thu, 5 Sep

Tutorial on Testing of Machine Learning Applications

Sandya Mannarswamy (@sandyasm)

  • 0 comments
  • Tue, 3 Sep

Rigorous Evaluation of NLP Models for Real World Deployment

Sandya Mannarswamy (@sandyasm)

  • 0 comments
  • Tue, 3 Sep

PySpark for GeoSpatial Data

Prakhar Srivastava

  • 2 comments
  • Mon, 19 Aug

Using NLP to generate Quizzes

Vishal Gupta (@vizgupta)

  • 1 comments
  • Tue, 30 Jul

Deploying Deep Learning models on the Edge (Android, IOS, ...)

A Naveen Kumar (@4nonymou5)

  • 0 comments
  • Fri, 19 Jul

Machine Learning Model and Dataset Versioning

Kurian Benoy (@kurianbenoy)

  • 0 comments
  • Thu, 18 Jul

Building a time series model using CNNs and GANs

prasenjeet acharjee (@pac1310)

  • 0 comments
  • Mon, 24 Jun

Design a real-time anomaly detection application using Spark and Machine Learning

ANKIT JAIN (@ankitjain22)

  • 1 comments
  • Sat, 15 Jun

iCASSTLE: Imbalanced Classification Algorithm for Semi Supervised Text Learning

Debanjana Banerjee (@debanjana)

  • 12 comments
  • Sat, 15 Jun

Generation of Newsletter using Natural Language Generation

Rajesh Gudikoti (@ragudiko)

  • 2 comments
  • Mon, 10 Jun

Time Series Anomaly detection on structured data from IOT Network using CNN

prasenjeet acharjee (@pac1310)

  • 0 comments
  • Tue, 21 May

Network health predictions and optimization recommendation using Deep learning Neural network models and Reinforcement learning

Anuradha K (@anuradhak)

  • 15 comments
  • Tue, 7 May

Advanced NLP and Deep Learning for document classification - A case study in civil aviation safety prognosis

prabhakar srinivasan (@prabhacar7)

  • 3 comments
  • Wed, 1 May

Learning to Rank framework for product recommendation - Ranknet to LambdaMART to Groupwise scoring functions - experiments

narasimha m (@6544)

  • 4 comments
  • Wed, 1 May

Dataset Denoising : Improving Accuracy of NLP Classifier

Khaleeque Ansari (@khaleeque-ansari)

  • 3 comments
  • Tue, 30 Apr

Taking AI Products to Market

Puneeth N (@puneethnarayana)

  • 3 comments
  • Tue, 30 Apr

Practical Recommendation Systems: Scalability, Accuracy, Latency

Kunal Kishore (@kunalkishore)

  • 0 comments
  • Tue, 30 Apr

GAN-inspired Innovations in Computer Vision

Pushkar Pushp (@ppushp7)

  • 1 comments
  • Tue, 30 Apr

End to End Computer Vision paradigm with respect to advanced deep learning techniques.

Pushkar Pushp (@ppushp7)

  • 2 comments
  • Tue, 30 Apr

Large scale Machine Learning and data storage for CDP: transforming Digital Marketing

Kunal Kishore (@kunalkishore)

  • 1 comments
  • Tue, 30 Apr

Modeling the effects of blurriness in mobile ads

Abhijith C (@abhijith-c)

  • 1 comments
  • Tue, 30 Apr

Bridging the gap between research to the deployment of Machine Learning models

Nilesh Patil (@nilesh-patil)

  • 3 comments
  • Tue, 30 Apr

Opening the Black Box: How to Interpret Machine Learning models; techniques, tools, and takeaways

Farhat Habib (@distantfedora)

  • 2 comments
  • Tue, 30 Apr

Recommendation @ Scale

Aditya Patel (@adityap)

  • 2 comments
  • Tue, 30 Apr

Using Locations for Online-Behaviour Prediction with Sparse Data

Nishant Oli (@nishantoli)

  • 2 comments
  • Tue, 30 Apr

Using AI for improving performance and design of ad creatives

Farhat Habib (@distantfedora)

  • 4 comments
  • Tue, 30 Apr

Tools for AI & ML for machine learning at Scale.

Saurabh Misra (@saurabh-appd)

  • 1 comments
  • Tue, 30 Apr

Feature selection and engineering using genetic algorithms and genetic programming

SIDHARTH KUMAR (@sidkumar)

  • 3 comments
  • Tue, 30 Apr

Explainable AI: Behind the Scenes

Manjunath (@manjunath-123)

  • 6 comments
  • Tue, 30 Apr

Interpret-ability as a bridge from Insights to Intuition in Machine and Deep Learning

Sai Sundarakrishna (@psgsai)

  • 1 comments
  • Mon, 29 Apr

Out of Distribution Detection in Deep Learning Classifiers

Akhil Lohia (@alohia)

  • 3 comments
  • Mon, 29 Apr

Learning to Rank recommendation - Ranknet to LambdaMART to Groupwise scoring functions - experiments, introduction to Tensorflow Ranking

narasimha m (@6544)

  • 5 comments
  • Mon, 29 Apr

Reinforcement Learning

Suraj Sheth (@shethsh)

  • 1 comments
  • Mon, 29 Apr

Introduction to Bayesian Networks(3 hour workshop)

usha rengaraju (@usharengaraju)

  • 5 comments
  • Sun, 28 Apr

Hands on Deep Learning for Computer Vision – Techniques for Image Segmentation.(6 hours workshop).

usha rengaraju (@usharengaraju)

  • 4 comments
  • Sun, 28 Apr

Tensorboard: Almost a one stop shop for Machine Learning Development

Tushar Pawar (@tuuushaar)

  • 5 comments
  • Sun, 28 Apr

Annotate This! A simple tool that seeks to simplify the creation of annotated text datasets - primarily for Hindi and other vernacular Languages

karmanya aggarwal (@calmdownkarm)

  • 3 comments
  • Sun, 28 Apr

Productionizing deep learning workflow with Hangar, <frameworkOfYourChoice> & RedisAI

Sherin Thomas (@hhsecond)

  • 4 comments
  • Fri, 26 Apr

Snorkeling in the deep: Bootstrapping an NLU model

Shubhangi Agrawal (@shubhangia)

  • 3 comments
  • Thu, 25 Apr

Non-Intent User Similarity for recommendation systems

Gunjan Sharma (@gunjan-sharma)

  • 1 comments
  • Thu, 25 Apr

Weaponising Artificial Intelligence In Cyber Security
 - The Next Age of Cyber Security Endgame

Vanshit Malhotra (@vanshit)

  • 0 comments
  • Fri, 19 Apr

AgentBuddy: Leveraging Bandit Algorithms for a human-in-loop system for Customer Care Agents (Paper accepted for the demo track at SIGIR-2019)

Mithun Ghosh (@mithunghosh)

  • 2 comments
  • Wed, 17 Apr

A Guide on Dynamic Parameter Estimation for Causal Forecasting

Tanmoy Bhowmik (@tanmoyb)

  • 3 comments
  • Tue, 16 Apr

Accountable Behavioural Change Detection (VEDAR) using Machine Learning

Srinivasa Rao Aravilli (@aravilli)

  • 9 comments
  • Tue, 16 Apr

Use cases of Financial Data Science Techniques in retail

Sudipto Pal (@sudipto-pal)

  • 3 comments
  • Mon, 15 Apr

Email Data Analytics

Rahul Sharma (@rahulks)

  • 3 comments
  • Mon, 15 Apr

Yes! Attention is all you need for NLP

simrat (@sims)

  • 8 comments
  • Sun, 14 Apr

Building a Context-Aware Knowledge graph using Graph analysis & Language models

Shashank Rao (@shashankpr)

  • 1 comments
  • Sun, 14 Apr

Introduction to Probabilistic Programming - PyMC3 and Edward

Hariharan C (@harc)

  • 6 comments
  • Sat, 13 Apr

Hacking Self-attention architectures to address Unsupervised text tasks

Venkata Dikshit Pappu (@vdpappu)

  • 8 comments
  • Thu, 11 Apr

Unsupervised Catalog Generation with Clustering, Reinforcement and More

Govind Chandrasekhar (@gc20)

  • 5 comments
  • Fri, 5 Apr

Industrialized Capsule Networks for Text Analytics

Vijay Srinivas Agneeswaran, Ph.D (@vijayagneeswaran)

  • 10 comments
  • Wed, 3 Apr

Bandit algorithms to Reduce Cognitive Load on Customer Care Agents (Paper accepted for the demo track at SIGIR-2019)

Hrishi Ganu (@blah)

  • 6 comments
  • Tue, 26 Mar

Exploring the un-conventional: End-to-End learning architectures for automatic speech recognition

Vikram Vij (@vikramvij)

  • 4 comments
  • Sun, 17 Mar

Essential Python Recipes for Deep Learning

Aakash N S (@aakashns)

  • 1 comments
  • Tue, 26 Feb

Production Object Detection - A Journey of Training, Building and Deploying CV models

Tarang Shah (@tarang27)

  • 0 comments
  • Sat, 20 Oct

Virtual Assistant for High Volume Recruitment

Piyush Makhija (@piyushmakhija)

  • 0 comments
  • Mon, 15 Oct

Portfolio Optimization using Deep Reinforcement Learning

Sonam Srivastava (@sonaam1234)

  • 3 comments
  • Sun, 19 Aug