About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to email@example.com
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Anthill Inside 2019 sponsors:
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 investigation and their corresponding concise event sequences. Classification models are developed for Class A passenger air carriers, that take either an observed sequence of events or the corresponding raw text narrative as input and make predictions regarding whether an accident or an incident is the likely outcome, whether the aircraft would be damaged or not and whether any fatalities are likely or not.
Sequential models for NLP are gaining popularity and this presentation talks about a case-study of applying these techniques to solve real problems for the Civil Aviation in the US. The classification models are developed using Word Embedding and the Long Short-term Memory (LSTM) algorithm. The proposed methodology is implemented in two steps: (i) transform the NTSB data extracts into labeled datasets for building supervised machine learning models; and (ii) develop DL models for doing prognosis of adverse events like accidents, aircraft damage or fatalities. We also develop a prototype for an interactive query interface for end-users to test various scenarios including complete or partial event sequences or narratives and get predictions regarding the adverse events. The presentation is accompanied by a demo component and the resulting F1-score metrics are used to evaluate the effectiveness of the technique. Audience will gain in-depth insight into the technology stack used for this deep learning application and the ways to troubleshoot the usual problems of noise in natural language.
Prabhakar Srinivasan as obtained a Masters in Computer Science from DePaul university, Chicago and has over 13 years industry experience working for companies like Apple, Yahoo!, and Cisco and start-ups like Coffeemeetsbagel. With a breath of experience in developing Enterprise-scale applications like Recommendation Engines and Deep-Learning applications for Forecasting Sales and Demand Prediction in Supply Chain, the author has in-depth knowledge of the tools and technologies used for developing pragmatic machine learning applications.