Submissions

Anthill Inside 2019

A conference on AI and Deep Learning

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Submissions are closed for this project

Taj M G Road, Bangalore, Bangalore

Submission guidelines

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.expand

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.

Contact details:

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

Make a submission

Submissions are closed for this project

Submissions

PM

Piyush Makhija

Virtual Assistant for High Volume Recruitment

Logistics companies, both old and new, have invested heavily in building an efficient frontline workforce to provide swift and convinient services to their users. Timely delivery is often a critical deciding factor for the ever-impatient customers to choose service A over service B. Hence, operations/logistic team is the key enabler here. more
  • 0 comments
  • Rejected
  • Mon, 15 Oct
Technical level: Intermediate
SS

Sonam Srivastava

Portfolio Optimization using Deep Reinforcement Learning

What is Portfolio Management? What is Deep Learning? How does one apply deep learning to the complex problem of portfolio management? What is the intuitive interpretation of this application? What lies under the black box? more
  • 3 comments
  • Rejected
  • Sun, 19 Aug
Section: Full talk Technical level: Advanced Session type: Demo
TS

Tarang Shah

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

Computer Vision as a field has changed manifold in the past few years. Researchers publish their papers and at times their code for the latest algorithms, but the challenge for the industry remains in applying that research to their processes. Customising a company’s proprietary data for the research models, implementing their code, and training models is the first big hurdle. Then comes the part… more
  • 0 comments
  • Rejected
  • Sat, 20 Oct
Technical level: Beginner
AS

Aakash N S

Essential Python Recipes for Deep Learning

This coding-focused talk offers some practical tips and advice that can help beginners be more efficient, organized and productive while working on deep learning projects and participating in data science competitions. The topics covered include working with the file system, effecting processing CSV files, doing exploratory data analysis, creating a baseline model for evaluation, and easily worki… more
  • 1 comments
  • Rejected
  • Tue, 26 Feb
Section: Crisp talk Technical level: Beginner
VV

Vikram Vij

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

Speech recognition is a challenging area, where accuracies have risen dramatically with the use of deep learning over the last decade, but there are still many areas of improvement. We start with the basics of speech recognition and the design of a conventional speech recognition system, comprising of acoustic modeling, language modeling, lexicon (pronunciation model) and decoder. To improve the … more
  • 4 comments
  • Rejected
  • Sun, 17 Mar
Section: Full talk Technical level: Intermediate Session type: Lecture
MG

Mithun Ghosh

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

We have developed a human-in-the loop system, AgentBuddy that is helping Intuit improve the quality of search it offers to internal Customer Care Agents (CCAs). AgentBuddy aims to reduce the cognitive effort on part of the CCAs while at the same time boosting the quality of our legacy federated search system. It addresses two key pain points 1)Given several candidate query answering mechanisms, h… more
  • 2 comments
  • Rejected
  • Wed, 17 Apr
Section: Full talk Technical level: Intermediate Session type: Demo
VM

Vanshit Malhotra

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

My talk discusses my research on how to integrate AI technologies into cyber security industry intelligently. more
  • 0 comments
  • Rejected
  • Fri, 19 Apr
Section: Crisp talk Technical level: Intermediate
GS

Gunjan Sharma

Non-Intent User Similarity for recommendation systems

In the world of Ad Business’s recommendation systems it is easier comparatively to recommend to user who have shown some intent. But what about the users who have not shown any intent? How do you target them? In this talk I will like to talk about a novel approach to use user similarity from supply data to work out significant recommendation for these users more
  • 1 comments
  • Rejected
  • Thu, 25 Apr
Section: Full talk Technical level: Intermediate
SA

Shubhangi Agrawal

Snorkeling in the deep: Bootstrapping an NLU model

Consider building a natural language understanding model for powering task based conversational agents. One of the problems to be solved is slot extraction. For example, if a user utters “show me flights from bengaluru to delhi on 25th july”, the model needs to extract the slots {from: bengaluru, to: delhi, date: 25-07-2019}. Recent advances in deep learning can solve this problem with adequate t… more
  • 3 comments
  • Rejected
  • Thu, 25 Apr
Section: Crisp talk Technical level: Beginner Session type: Lecture
MG

Madhu Gopinathan

Attention based sequence to sequence models for natural language processing

Workshop details including schedule, venue, date and tickets are published here: https://hasgeek.com/anthillinside/sequence-to-sequence-models-workshop/ more
  • 1 comments
  • Confirmed
  • Fri, 26 Apr
Section: Workshops Technical level: Intermediate Session type: Workshop
ST

Sherin Thomas

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

Managing DL workflow is always a nightmare. Problems include handling the scale, efficient resource utilization, version controlling the data etc. With the heavily organized Hangar, we can keep the data on check now, not as a blob but as tensors in the data store and version at. The super flexible PyTorch gives us the advantage of prototyping faster and iterate smoother. The model prototype can n… more
  • 4 comments
  • Rejected
  • Fri, 26 Apr
Technical level: Intermediate Section: Workshops Session type: Workshop
ka

karmanya aggarwal

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

This talk will comprise of my giving a quick overview of an open source tool that allows for faster and relatively error free annotations of text data, by leveraging language models and gamification principles. I would subsequently go over the tech that powers it and demonstrate how it works. more
  • 3 comments
  • Confirmed
  • Sun, 28 Apr
Section: Crisp talk Technical level: Beginner Session type: Demo
TP

Tushar Pawar

Tensorboard: Almost a one stop shop for Machine Learning Development

This talk will focus on the areas of Machine Learning development (specifically Computer Vision problems) and will discuss how using certain tools will make your life easier. Also, discuss some areas where developing custom tools is beneficial instead of using open-source tools and the trade-off for making this choice. We will discuss why we should be using Tensorboard and why it is the only tool… more
  • 9 comments
  • Confirmed & scheduled
  • Sun, 28 Apr
Section: Full talk Technical level: Beginner Session type: Lecture
ur

usha rengaraju

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

Abstract – Computer Vision has lots of applications including medical imaging, autonomous vehicles, industrial inspection and augmented reality. Use of Deep Learning for computer Vision can be categorized into multiple categories for both images and videos – Classification, detection, segmentation & generation. Having worked in Deep Learning with a focus on Computer Vision have come across variou… more
  • 5 comments
  • Rejected
  • Sun, 28 Apr
Section: Workshops Technical level: Intermediate Session type: Workshop
ur

usha rengaraju

Introduction to Bayesian Networks(3 hour workshop)

Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein e… more
  • 5 comments
  • Rejected
  • Sun, 28 Apr
Section: Workshops Technical level: Beginner Session type: Workshop
SS

Suraj Sheth

Reinforcement Learning

We see quite a lot of excitement around Reinforcement Learning (RL) in industry and academia. We regularly see new industrial applications both inside and outside of Amazon. We also see RL papers dominating many ML conferences such as NeurIPS and ICML. RL is promising for multiple reasons that include a better formulation of the problem which allows for optimization of multi-step decision making … more
  • 1 comments
  • Under evaluation
  • Mon, 29 Apr
Section: Workshops Technical level: Intermediate Session type: Workshop
nm

narasimha m

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

Search and product recommendations are typically served using CF, MF, FM techniques, content/context/sequence based methods or using learning to rank framework which is more generic than rest. Evolving from traditional classification and regression modeling methods, the loss functions, gradients, computation tricks have evolved to suit ranking problems (point wise to pair wise to list wise soluti… more
  • 5 comments
  • Rejected
  • Mon, 29 Apr
Section: Full talk Technical level: Intermediate Session type: Lecture
AL

Akhil Lohia

Out of Distribution Detection in Deep Learning Classifiers

A common problem when using deep neural network models for classification problems is handling out of distribution data. In such scenarios, the classifiers tend to assign the new data point to one of the known classes with high probability, which can lead to unintended and potentially harmful consequences. At MakeMyTrip (MMT), we use deep learning based NLP classifier for understanding intent of … more
  • 3 comments
  • Rejected
  • Mon, 29 Apr
Section: Crisp talk Technical level: Intermediate Session type: Lecture
SS

Sai Sundarakrishna

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

Requisite characteristics of the Machine Learning models that make them fully deployable in a business setting are multivarious and sometimes compelling. Mere predictive power and validation accuracy are sometimes not sufficient. These models need to be interpretable, bias-free, transparent, explainable, consistent and should have a global and local basis for their output predictions. In this tal… more
  • 1 comments
  • Rejected
  • Tue, 30 Apr
Section: Full talk Technical level: Intermediate Session type: Lecture
M

Manjunath

Explainable AI: Behind the Scenes

With the Ai based systems proliferating in to all applications of the daily life, getting a better insight of their working mechanism is a much sought-after affair. Often the rationale behind the decision thrown out by an AI system is not well understood. Which part or feature of the input has influenced the decision to what extent is not known. This presentation provides insights to demystify th… more
  • 6 comments
  • Rejected
  • Tue, 30 Apr
Section: Tutorials Technical level: Intermediate Session type: Tutorial
SK

SIDHARTH KUMAR

Feature selection and engineering using genetic algorithms and genetic programming

While feature selection is almost a solved problem in data science, feature engineering is still quite a mystery. In this talk I will outline a method that I use to solve feature engineering, with a goal to provide a generalized framework to tackle both feature engineering and selection simultaneoously. more
  • 3 comments
  • Rejected
  • Tue, 30 Apr
Section: Full talk Technical level: Advanced Session type: Lecture
SM

Saurabh Misra

Tools for AI & ML for machine learning at Scale.

As applications become more business critical and application teams are receiving monitoring data for these mission critical business applications as a continuous stream it becomes difficult to manually monitor them and create dashboards/reporting around these applications. more
  • 1 comments
  • Rejected
  • Tue, 30 Apr
Section: Full talk Technical level: Intermediate Session type: Lecture
FH

Farhat Habib

Using AI for improving performance and design of ad creatives

The ad creative is the image of the ad that the user sees on their device. It includes information such as the image dimensions, its placement on the screen or within the app, the native resolution of the creative and the mobile screen it is displayed on and similar parameters. Why exactly a particular ad is visually appealing and another not is hard to determine. Using data from the performance … more
  • 4 comments
  • Rejected
  • Tue, 30 Apr
Technical level: Intermediate Session type: Lecture Section: Crisp talk
NO

Nishant Oli

Using Locations for Online-Behaviour Prediction with Sparse Data

User behaviour varies with their current location, as a consequence their engagement with online media (say Ads) varies with where they are; Knowing the type of location can help us target the user better and recommend better. more
  • 2 comments
  • Under evaluation
  • Tue, 30 Apr
Section: Crisp talk Technical level: Intermediate Session type: Lecture
AP

Aditya Patel

Recommendation @ Scale

Recommendation is one of the most traditional and wide spread use case of Machine Learning. In this talk we want to showcase, how an advanced recommendation engine can be served at scale in Glance. Glance is an AI-powered, content driven, personalised Screen Zero (lockscreen) platform for mobile, which is used by over 26M DAU users in India. The talk will take you through each component of a reco… more
  • 2 comments
  • Rejected
  • Tue, 30 Apr
Section: Crisp talk Technical level: Intermediate Session type: Lecture
FH

Farhat Habib

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

Interpretability of a model is the degree to which a human can consistently predict the model’s result. The higher the interpretability of a machine learning model, the easier it is to comprehend why certain decisions or predictions have been made. While interpretability is not important in low-risk domains and black box models abound, in domains such as medicine or finance, or high-risk domains … more
  • 2 comments
  • Rejected
  • Tue, 30 Apr
Section: Workshops Technical level: Intermediate Session type: Workshop
NP

Nilesh Patil

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

In this talk, we propose a way to minimize the effort required to move data munging, visualization and training for ML models into production. Python notebooks are a go-to tools to carry out such research. Moving from this painstaking research to a full blown deployment requires too much effort and care from a data scientist’s perspective. Attempting to make the modeling process amiable to produc… more
  • 3 comments
  • Rejected
  • Tue, 30 Apr
Section: Full talk Technical level: Intermediate Session type: Workshop
AC

Abhijith C

Modeling the effects of blurriness in mobile ads

The creative is the image of the ad that the user sees and engages with upon viewing. This talk studies the effect of an ad creative’s specifications and quality of render in performance campaigns and suggests a playbook for digital marketers based on the findings and insights. An offline study on an ad creative’s specifications such as resolution, aspect ratio, handset density, device orientatio… more
  • 1 comments
  • Rejected
  • Tue, 30 Apr
Section: Crisp talk Technical level: Intermediate Session type: Lecture
KK

Kunal Kishore

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

We will talk about why do we need a single end-to-end customer data platform to enable truly personalised digital marketing. We also explain what pain-points, such as cold-start problem, do we solve for marketers if we collate and utilise data from first, second and third party sources rather than relying on just first party data. Then we will focus on the motive to use Machine Learning to create… more
  • 1 comments
  • Rejected
  • Tue, 30 Apr
Technical level: Intermediate Session type: Lecture Section: Full talk
PP

Pushkar Pushp

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

Deep learning based approaches to solve image classification have become a core technology in AI, largely due to developments in computing powers and digital data. However image classification gained popularity beyond academic circle with the advent of visual object recognition challenge. more
  • 2 comments
  • Rejected
  • Tue, 30 Apr
Technical level: Intermediate Section: Tutorials Session type: Tutorial
PP

Pushkar Pushp

GAN-inspired Innovations in Computer Vision

“The most interesting idea in the last 10 years in ML.” - Yann LeCun, Facebook AI research director. more
  • 1 comments
  • Rejected
  • Tue, 30 Apr
Technical level: Intermediate Session type: Lecture Section: Tutorials
KK

Kunal Kishore

Practical Recommendation Systems: Scalability, Accuracy, Latency

This tutorial shall cover traditional and modern recommendation systems from a perspective of practical application, in an easy question-answer format. The content of this tutorial is derived from multiple state-of-the-art research papers as well as classical text books on recommendation systems. more
  • 0 comments
  • Rejected
  • Tue, 30 Apr
Section: Tutorials Technical level: Intermediate Session type: Tutorial
PN

Puneeth N

Taking AI Products to Market

Want to have specific focussed discussion on what it takes to take pure play AI products to market. Have first hand experience taking multiple AI products to Indian B2B Market, I wanted to get product managers and KBLs in the domain to come in and share their journey and experiences. Hence, suggesting a bird of feather session. more
  • 3 comments
  • Rejected
  • Tue, 30 Apr
Section: Birds Of Feather (BOF) session Technical level: Intermediate Session type: Lecture
KA

Khaleeque Ansari

Dataset Denoising : Improving Accuracy of NLP Classifier

Reliable evaluation for the performance of classifiers depends on the quality of the data sets on which they are tested. During the collecting and recording of a data set, however, some noise may be introduced into the data, especially in various real-world environments, which can degrade the quality of the data set. In this talk we will discuss how we at MakeMyTrip are continuously improving per… more
  • 3 comments
  • Under evaluation
  • Tue, 30 Apr
Section: Crisp talk Technical level: Intermediate Session type: Lecture
SP

Sudipto Pal

Use cases of Financial Data Science Techniques in retail

Financial domains like Insurance and Banking have uncertainty itself as an inherent product feature, and hence makes extensive use of Statistical models to develop, valuate and price their products. This presentation will showcase some of the techniques like Survival models and cashflow prediction models, popularly used in financial products, how can they be used in Retail data science, by showca… more
  • 3 comments
  • Rejected
  • Mon, 15 Apr
Section: Crisp talk Technical level: Intermediate Session type: Lecture
RS

Rahul Sharma

Email Data Analytics

Sentiment Analysis from text is a well known problem in machine learning where a given text document can be either positive, negative or neutral. In the last few years, Sentiment Analysis has become a hot-trend topic of scientific and market research in the field of Natural Language Processing (NLP) and Machine Learning. more
  • 3 comments
  • Rejected
  • Mon, 15 Apr
Session type: Lecture
SR

Shashank Rao

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

Introduction At EtherLabs, we are building a video platform that provides insights into the numerous live audio and video meetings that organizations conduct everyday. In such a scenario, in order to acquire critical metrics such as important moments, topics discussed and possible intents from textual data, basic NLP tasks like keyphrase extraction becomes significantly important. Important keyph… more
  • 2 comments
  • Rejected
  • Sun, 14 Apr
AK

Anuradha K

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

Time series prediction of network parameters and detecting network health with network performance optimization, has been an interesting problem to solve for researchers in the field of Machine Learning and Data Mining community. These use cases are present across different industries like retail, telecom, transport with good presence in Telecom industry. However, there remains a challenge in get… more
  • 15 comments
  • Rejected
  • Tue, 07 May
Section: Workshops Technical level: Intermediate Session type: Workshop
HC

Hariharan C

Introduction to Probabilistic Programming - PyMC3 and Edward

Probabilistic programming differ from deterministic ones by allowing language primitives to be stochastic. In other words, instead of being restricted to deterministic assignments such as: more
  • 6 comments
  • Rejected
  • Sat, 13 Apr
Section: Tutorials Technical level: Intermediate Session type: Tutorial
ps

prabhakar srinivasan

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

In this presentation, I apply a set of data-mining and sequential deep learning techniques to accident reports published by the National Transportation Safety Board (NTSB), in order to support real-time prognosis of adverse events. The focus here is on learning with text data that describes sequences of events. NTSB creates post-hoc investigation reports which contain raw text narratives of their… more
  • 3 comments
  • Rejected
  • Thu, 02 May
Section: Tutorials Technical level: Intermediate Session type: Tutorial
VP

Venkata Dikshit Pappu

Hacking Self-attention architectures to address Unsupervised text tasks

Self-attention architectures like BERT, OpenAI GPT, MT-DNN are current state-of-the art feature extractors for several supervised downstream tasks for text. However, their ability on unsupervised tasks like document/sentence similarity are inconclusive. In this talk, I intend to cover brief overview of self attention architectures for Language Modelling, fine-tuning/feature selection approaches f… more
  • 8 comments
  • Rejected
  • Thu, 11 Apr
VP

Vijay Srinivas Agneeswaran, Ph.D

Industrialized Capsule Networks for Text Analytics

Multi-label text classification is an interesting problem where multiple tags or categories may have to be associated with the given text/documents. Multi-label text classification occurs in numerous real-world scenarios, for instance, in news categorization and in bioinformatics (gene classification problem, see [Zafer Barutcuoglu et. al 2006]). Kaggle data set is representative of the problem: … more
  • 10 comments
  • Rejected
  • Wed, 03 Apr
AG

Anuj Gupta

NLP bootcamp

Recent advances in machine learning have rekindled the quest to build machines that can interact with outside environment like we human do - using visual clues, voice and text. An important piece of this trilogy are systems that can process and understand text in order to automate various workflows such as chat bots, named entity recognition, machine translation, information extraction, summariza… more
  • 0 comments
  • Confirmed
  • Wed, 15 May
Session type: Workshop
TB

Tanmoy Bhowmik

A Guide on Dynamic Parameter Estimation for Causal Forecasting

Executives now-a-days rely on forecasting approcahes in virtually any decision making. The use cases are ubiquitous in business and technology domains ranging from Demand/Sales forecasting in Supply Chain Management, Hiring/Attrition rate forecasting in HR operations, Predicting the cell traffic or netwrok health parameters for a Telecom business, just to name a few. The challenges faced in forec… more
  • 3 comments
  • Rejected
  • Tue, 16 Apr
Session type: Lecture
SA

Srinivasa Rao Aravilli

Accountable Behavioural Change Detection (VEDAR) using Machine Learning

With exponential increase in the availability of telemetry / streaming / real-time data, understanding contextual behavior changes is a vital functionality in order to deliver unrivalled customer experience and build high performance and high availability systems. Real-time behavior change detection finds a use case in number of domains such as social networks, network traffic monitoring, ad exch… more
  • 15 comments
  • Confirmed & scheduled
  • Tue, 16 Apr
Session type: Short talk of 20 mins
HG

Hrishi Ganu

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

We will describe a human-in-the loop system, AgentBuddy that is helping Intuit improve the quality of search it offers to internal Customer Care Agents (CCAs). AgentBuddy aims to reduce the cognitive effort on part of the CCAs while at the same time boosting the quality of our legacy federated search system. It addresses two key pain points 1)Given several candidate query answering mechanisms, ho… more
  • 6 comments
  • Rejected
  • Tue, 26 Mar
Session type: Full talk of 40 mins
RG

Rajesh Gudikoti

Generation of Newsletter using Natural Language Generation

We will generate event narratives using NLG. Assume I am part of event (like Anthill Inside). I conducted a session on particular topic (NLG). I make entry into internal tool. Below information are captured in database. more
  • 2 comments
  • Rejected
  • Mon, 10 Jun
Section: Crisp talk Technical level: Beginner Session type: Lecture
nm

narasimha m

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

Search and product recommendations are typically served using CF, MF, FM techniques, content/context/sequence based methods or using learning to rank framework which is more generic than rest. Evolving from traditional classification and regression modeling methods, the loss functions, gradients, computation tricks have evolved to suit ranking problems (point wise to pair wise to list wise soluti… more
  • 4 comments
  • Rejected
  • Wed, 01 May
Session type: Full talk of 40 mins
AJ

ANKIT JAIN

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

Our team works on a streaming application that produces outputs in the form of real-time comparisons between us and our competitors. The comparisons are consumed by our market managers and hotel partners and are used by them for making their day-to-day decisions and prioritize their business actions for the day. Owing to the importance and scale of the data, it is important to ensure that our Str… more
  • 2 comments
  • Rejected
  • Sat, 15 Jun
Section: Crisp talk Technical level: Beginner Session type: Discussion
pa

prasenjeet acharjee

Building a time series model using CNNs and GANs

Time series anomaly detection and classification problems have existed and there are various existing solutions to tackle such kind of problems. However, all/most of the solutions are for ideal cases (having enough labelled data or developing a model with average precision/recall) and does not take into account the practical constraints of implementing and deploying a highly generalizable solutio… more
  • 1 comments
  • Under evaluation
  • Mon, 24 Jun
Session type: Short talk of 20 mins
pa

prasenjeet acharjee

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

Time series anomaly detection and classification problems have existed and there are various existing solutions to tackle such kind of problems. However, all/most of the solutions are for ideal cases (having enough labelled data or developing a model with average precision/recall) and does not take into account the practical constraints of implementing and deploying a highly generalizable solutio… more
  • 0 comments
  • Rejected
  • Tue, 21 May
Session type: Short talk of 20 mins
KB

Kurian Benoy

Machine Learning Model and Dataset Versioning

In this talk we will discuss about the current practices of organizing ML projects using traditional open-source tool set like Git and Git-LFS as well as this tool set limitation. Thereby motivation for developing new ML specific version control systems will be explained. more
  • 1 comments
  • Under evaluation
  • Thu, 18 Jul
Section: Crisp talk Technical level: Intermediate Session type: Lecture
DB

Debanjana Banerjee

iCASSTLE: Imbalanced Classification Algorithm for Semi Supervised Text Learning

Information in the form of text can be found in abundance in the web today, which can be mined to solve multifarious problems. Customer reviews, for instance, flow in across multiple sources in thousands per day which can be leveraged to obtain several insights. Our goal is to extract cases of a rare event e.g., recall of products, allegations of ethics or, legal concerns or, threats to product-s… more
  • 13 comments
  • Rejected
  • Sat, 15 Jun
Session type: Short talk of 20 mins Section: Full talk Technical level: Advanced Session type: Lecture
VG

Vishal Gupta

Using NLP to generate Quizzes

The Problem Quizzes/Questions are required by students for self-evaluation and by teachers to test students. more
  • 1 comments
  • Rejected
  • Tue, 30 Jul
Section: Full talk Technical level: Intermediate Session type: Lecture
s

simrat

Yes! Attention is all you need for NLP

Natural language processing a very tough problem to crack. We humans have our own style of speaking and though many a times we might mean the same thing we say it differently. This makes it very difficult for the machine to understand and process language at human level. more
  • 8 comments
  • Rejected
  • Sun, 14 Apr
Session type: Lecture Session type: Full talk of 40 mins
JJ

Jacob Joseph

via Abhishek Balaji (@booleanbalaji)

Birds of a Feather on Interpretability

Complex machine learning models work very well at prediction and classification tasks but become really hard to interpret. On the other hand simpler models are easier to interpret but less accurate and hence oftentimes we are made to take a call between interpretability and accuracy. more
  • 0 comments
  • Confirmed & scheduled
  • Tue, 13 Aug
Section: Birds Of Feather (BOF) session Technical level: Intermediate Session type: Discussion
VG

Vijay Gabale

Myths and Realities of Data Labeling for Deep Learning

In this BoF, we will explore data labeling tasks for NLP and CV problems. Specifically, we will discusses nuiances around defining, crowd sourcing and executing data labeling tasks, along with quality assurance processes. We shall also discuss machine aided data taggint to save cost, time and efforts on different data labeling tasks. Finally, we shall also touch upon feedback loopswhen some of th… more
  • 0 comments
  • Confirmed & scheduled
  • Thu, 15 Aug
Section: Birds Of Feather (BOF) session Technical level: Intermediate Session type: Discussion
US

Uma Sawant

Demystifying deep reinforcement learning

Details (date, time, venue) and tickets for this workshop are available here: https://hasgeek.com/anthillinside/deep-reinforcement-learning-workshop/ more
  • 0 comments
  • Confirmed
  • Thu, 15 Aug
Section: Workshops Technical level: Intermediate Session type: Workshop
PS

Prakhar Srivastava

PySpark for GeoSpatial Data

GeoSpatial data is the key data source when it comes to external data sources but this data is often too large to process. This is where PySpark comes in to reduce the computation time and makes the whole code more than 5 times faster. This workshop aims to solve the problem of calculating land covered with greenry of a region (Delhi) using Satellite images and Python. more
  • 2 comments
  • Awaiting details
  • Tue, 20 Aug
Section: Tutorials Technical level: Beginner Session type: Tutorial
lT

lavanya TS

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

Machine learning (ML) has seen substantial adoption, and a large number of data science teams are being created. Taking on ML projects requires product managers and engineers to learn an ML approach to problem solving, to be able to effectively work with data scientists and data engineers. There exists a huge gap in understanding - both cultural & technical - in most organizations since product o… more
  • 0 comments
  • Confirmed
  • Tue, 20 Aug
Section: Workshops Technical level: Intermediate Session type: Workshop
WP

Willem Pienaar

Feast: Feature Store for Machine Learning

Features are key to driving impact with AI at all scales, allowing organizations to dramatically accelerate innovation and time to market. Willem Pienaar explain how GOJEK, Indonesia’s first billion-dollar startup, unlocked insights in AI by building a feature store called Feast, and some of the lessons they learned along the way. more
  • 1 comments
  • Confirmed & scheduled
  • Tue, 20 Aug
Section: Full talk Technical level: Beginner Session type: Lecture
NH

Nischal HP

Document digitization - Rethinking it with Deep Learning

When you think about Document digitisation from a business optimization process perspective, just performing OCR does not truly solve the problem. We at omni:us are building AI systems to support the insurance industry by handling claims. In order to achieve this we are performing various human-esque activities on so many different types of documents like page / document classification, informati… more
  • 2 comments
  • Confirmed & scheduled
  • Mon, 26 Aug
Section: Full talk Technical level: Intermediate Session type: Lecture
SM

Sandya Mannarswamy

Rigorous Evaluation of NLP Models for Real World Deployment

Rapid progress in NLP Research has seen a swift translation to real world commercial deployment. While a number of success stories of NLP applications have emerged, failures of translating scientific progress in NLP to real-world software have also been considerable (some of these issues are covered in my IJCAI paper https://www.ijcai.org/proceedings/2018/717). Specifically, the challenges and ga… more
  • 1 comments
  • Confirmed & scheduled
  • Tue, 03 Sep
Section: Full talk Technical level: Intermediate Session type: Discussion
SM

Sandya Mannarswamy

Tutorial on Testing of Machine Learning Applications

URL for workshop date, time, venue, schedule and tickets: https://hasgeek.com/anthillinside/testing-machine-learning-applications-workshop/ more
  • 0 comments
  • Confirmed
  • Tue, 03 Sep
Section: Tutorials Technical level: Intermediate Session type: Tutorial
ST

Sherin Thomas

Hangar; git for your data

Software development is entering an era where the behavior of programs critically depends on the data they were trained on. In this setting, data is the new source code, and this opens the door to challenges like versioning and collaboration on numerical data. Enter Hangar, an open-source tool by [tensor]werk that brings Git-style version control to n-dimensional arrays. It supports versioning, b… more
  • 4 comments
  • Waitlisted
  • Thu, 05 Sep
Section: Full talk Technical level: Intermediate Session type: Demo
GC

Govind Chandrasekhar

Unsupervised Catalog Generation with Clustering, Reinforcement and More

This presentation will look at how you can generate product catalogs from ecommerce websites using just the homepage URL of the website. Techniques explored include URL clustering, regex generation, reinforcement learning and supervised classification. more
  • 5 comments
  • Rejected
  • Fri, 05 Apr
AK

Ashish Kulkarni

Probabilistic Modeling – a tutorial on Bayesian Networks

Details of the workshop (date, time, venue) and tickets available here: https://hasgeek.com/anthillinside/bayesian-networks-tutorial/ more
  • 0 comments
  • Confirmed
  • Sat, 07 Sep
Section: Tutorials Technical level: Intermediate Session type: Tutorial
PC

Priyanshu Chandra

Building a Recommendation Engine for diverse content and user behaviors

Good recommendation systems, as we all know, are a great way to acquire users, creating a delightful user experience for user engagement while driving incremental revenue. There is a lot of innovation and research in making recommendations systems understand the user preferences and hence personalise better. more
  • 1 comments
  • Rejected
  • Sun, 08 Sep
Section: Full talk Technical level: Intermediate Session type: Lecture
VM

Ved Mathai

How we built our own scalable, real-time framework to create and serve conversational systems.

Voice based systems are becoming pervasive in the world, and NLP systems that power these systems have the daunting task of understanding, with high precision and high coverage(recall), what a user says, and also, doing it in real time. There are many frameworks that help create these paths, and over time the building of conversational agents have been distilled into few primitives such as intent… more
  • 3 comments
  • Waitlisted
  • Sun, 15 Sep
Section: Full talk Technical level: Intermediate Session type: Lecture
ma

mira abboud

via Zainab Bawa (@zainabbawa)

Artificial Intelligence for automated investment

Neotic.ai introduced the use of AI for assisting investors in decision making. The technology behind it is based on machine learning algorithms for price patterns recognition, corporate fundamentals, and financial news analysis. The company offers customized AI to hedge funds as well as plug and play, fully automated trading strategies. more
  • 1 comments
  • Confirmed & scheduled
  • Mon, 14 Oct
Section: Full talk Technical level: Advanced Session type: Lecture
SG

Srikanth Gopalakrishnan

Sizing biological cells and saving lives using AI

AI techniques are finding applications in a wide range of applications.Crowd counting deep learning models have been used to count people, animals, and microscopic cells. This talk will introduce some novel crowd counting techniques and their applications. A pharma case study will be presented to show how it was used for drug discovery to bring about 98% savings in drug characterization efforts. more
  • 2 comments
  • Awaiting details
  • Fri, 18 Oct
Section: Birds Of Feather (BOF) session Technical level: Intermediate Session type: Demo
DJ

Divij Joshi

Model Interpretability, Explainable AI and the Right to Information

Issues of ‘explainability in AI’ have emerged as an important theme in the development of machine learning and statistical modelling. Most studies look at explainability through the lens of model interpretability, in order to understand underlying machine learning models better and improve them for better optimisation. However, there is limited relevance of this understanding of interpretability … more
  • 1 comments
  • Confirmed & scheduled
  • Fri, 18 Oct
Section: Crisp talk Technical level: Beginner Session type: Discussion
AB

Amit Baldwa

Blueprint for Building AI Products

IT Product Development is changing at a rapid pace. From being an enabler of existing products, AI and ML has taken a Centerstage in Product development. more
  • 2 comments
  • Awaiting details
  • Fri, 18 Oct
Section: Full talk Technical level: Intermediate Session type: Lecture
NK

Naman Kumar

via Zainab Bawa (@zainabbawa)

What can software learn from robots and math

In almost every aspect of life, there is a concept of state (for example, where are you right now?) and a concept of uncertainty (for example, how soon can you reach home?). And whenever there is uncertainty, there is a tool that can help you get a better estimate of that state. It is called a Kalman Filter, named after an American Mathematician, Rudolf Kalman. more
  • 4 comments
  • Confirmed & scheduled
  • Sun, 27 Oct
Section: Full talk Technical level: Advanced Session type: Lecture
NS

Nishant Sinha

via Zainab Bawa (@zainabbawa)

The shape of U

Tensors are the fundamental data structure for building modern machine learning programs and complex neural architectures. Unfortunately, the foundations of popular tensor libraries (numpy, tensorflow, pytorch) are hardly robust, e.g., tensor broadcasting rules are adhoc, and may cause surprising bugs. Further, the tensor library APIs expose low-level memory models to the developer, forcing them … more
  • 0 comments
  • Confirmed & scheduled
  • Sun, 27 Oct
Section: Crisp talk Technical level: Advanced Session type: Lecture
SM

Srujana Merugu

via Zainab Bawa (@zainabbawa)

ML application lifecycle: recommendations for each stage

Building good ML systems is not very unlike developing good software. Just as developing good software requires mastering not only programming theory, tools, and design patterns, but also the process of software development itself, building a good ML system entails familiarity with the ML application lifecycle. In this talk, we will discuss the various stages of ML application life cycle - proble… more
  • 1 comments
  • Confirmed & scheduled
  • Fri, 01 Nov
Section: Full talk Technical level: Intermediate Session type: Lecture
BS

Bikram Sengupta

Build an enterprise grade data labelling pipeline to scale your ML/AI pipelines

In Software 2.0, Data is code. A mindful approach to your data annotation pipeline and practices is critical to the outcomes of your ML algorithms. If not done right, your ability to scale this pipeline can often prove to be a major blocker to productionization. more
  • 0 comments
  • Confirmed & scheduled
  • Thu, 07 Nov
Session type: Full talk of 40 mins
KJ

Keshav Joshi

via Tarunima Prabhakar (@flicker91)

Open Source Tools and Archive for Tackling Misinformation on ChatApps in India

Tattle is a civic tech project in India that is creating an archive of content circulated on WhatsApp and other chat apps, and building open source tools to navigate this archive. Such an archive is useful for research on information networks as well as for increasing the efficiency and reach of fact checking efforts. One of Tattle’s goals is opening the archive, even if in a limited scope, to th… more
  • 0 comments
  • Confirmed & scheduled
  • Thu, 07 Nov
Session type: Short talk of 20 mins
RR

Radhika Radhakrishnan

Gendered Biases in Artificial Intelligence

My talk will attempt to bust the myth of “objectivity” or “neutrality” of Artificial Intelligence (AI) technologies by highlighting gendered biases in AI and how they arise. To substantiate, I will focus on Virtual Assistants / “Bots” by presenting my primary research on a comparative analysis of the responses of smartphone-based Virtual Assistants (such as Siri and Alexa) to user queries on gend… more
  • 0 comments
  • Confirmed & scheduled
  • Thu, 07 Nov
Session type: Full talk of 40 mins Section: Full talk Technical level: Intermediate Session type: Lecture
VR

Vinodh Kumar Ravindranath

How we applied sampling algorithms to extract meaning from data (@ Belong.co)

A lot of unsupervised learning algorithms work by inferencing parameters of generative models through Monte Carlo techniques. In this talk, we will go into details of the underlying inference algorithms that use sampling techniques and then proceed step-by-step applying it to couple of real world problems, particularly some of our work at Belong that we recently published at ICDAR‘19. The attende… more
  • 0 comments
  • Confirmed & scheduled
  • Thu, 07 Nov
Session type: Full talk of 40 mins
NM

Nickil Maveli

Efficient Machine Translation for low resource languages using Transformers

Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence aligned RNNs or convolution. Transformers were recently used by OpenAI in their language models, and also used recently by DeepMind for AlphaStar, their program to defeat a top professional Starcraft player. more
  • 0 comments
  • Under evaluation
  • Tue, 05 Nov
Session type: Full talk of 40 mins
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