##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.
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 exchange metrics etc. In streaming data, behavior change is an implausible observation that does not fit in with the distribution of rest of the data. A timely and precise revelation of such behavior changes can give us substantial information about the system in critical situations which can be a driving factor for vital decisions. Detecting behavior changes in streaming fashion is a difficult task as the system needs to process high speed real-time data and continuously learn from data along with detecting anomalies in a single pass of data. In this talk, we introduce a novel algorithm called Accountable Behavior Change Detection (VEDAR) which can detect and elucidate the behavior changes in real-time and operates in a fashion similar to human perception. We have bench marked our algorithm on open source anomaly detection datasets. We have bench marked our algorithm by comparing its performance on open source anomaly datasets against industry standard algorithms like Numenta HTM and Twitter AdVec (SH-ESD). Our algorithm outperforms above mentioned algorithms for behaviour change detection, efficacy
This talk mainly covers VEDAR algorithem in detail and benchmarks comparison with other streamingly anomoly detection. More details in the https://arxiv.org/abs/1902.06663
Aravilli Srinivasa Rao working as Sr. Engineering Manager in Cisco CTO group and leading innovation & incubation of ML and AI projects. As a speaker presented in following conferences/workshops
- Presented about Cisco’s ML/AI Applications in PDPC/CIPL workshop in Singapore. As a panelist shared experiences and thoughts on Accountable and Responsible AI. 2 ) Presented in IoT and AI Sumit organized by CII ’s in India about IoT and ML applications and related platforms in IoT space. 3) Presented about “Streaming Anomaly Detection” in Cisco’s Data Science Summit in Prague
He has a patent in Software recommendations uisng Reinforcement Learning.