##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 sales@hasgeek.com
#Sponsors:
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Anthill Inside 2019 sponsors:
#Bronze Sponsor
 |
 |
 |
#Community Sponsor
 |
 |
 |
 |
|
 |
 |
|
 |
 |
Hosted by
Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more
VR
Vinodh Kumar Ravindranath
@vinodh_kumar
How we applied sampling algorithms to extract meaning from data (@ Belong.co)
Submitted Nov 7, 2019
Session type:
Full talk of 40 mins
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 attendees, in addition to learning inferencing algorithms (such as Gibbs sampling) and various probabilistic models (Dirichlet, Wishart distributions, etc), will get a glimpse of how to model real world problems, apply ML algorithms and work through the practical challenges that are encountered while building a high quality data science product / solution.
Outline
Generative models => Basic idea of sampling algorithms to inference parameters => Simple example using Gibbs sampling => Application to a more complex problem of resume understanding at Belong.co
Speaker bio
Currently CTO at Belong.co, Vinodh Kumar is one of the top industry leaders with more than a decade of hands-on experience in search, ranking and machine learning. Prior to Belong, Vinodh used to be CTO/M.D of Bloomreach driving their e-commerce search engine efforts. Earlier Vinodh spent more than 6 years at Google leading the Google News team and building the ranking algorithms that power Google News. He did his masters in computer science from the Indian Institute of Science after securing the All India Rank #1 in Graduate Engineering Entrance Exam (GATE '99) in computer science. He has more than 10 patents to his name.
Links
Comments
Hosted by
Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more
{{ gettext('Login to leave a comment') }}
{{ gettext('Post a comment…') }}{{ errorMsg }}
{{ gettext('No comments posted yet') }}