166 citations · 515 across the 60 of their papers we have counts for
7 papers · 1 filter
Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization
Rishabh Iyer, Pratik Dubal, Kunal Dargan +3
With increasing amounts of visual data being created in the form of videos and images, visual data selection and summarization are becoming ever increasing problems. We present Vis…
A Unified Batch Online Learning Framework for Click Prediction
Rishabh Iyer, Nimit Acharya, Tanuja Bompada +2
We present a unified framework for Batch Online Learning (OL) for Click Prediction in Search Advertisement. Machine Learning models once deployed, show non-trivial accuracy and cal…
A Framework towards Domain Specific Video Summarization
Vishal Kaushal, Sandeep Subramanian, Suraj Kothawade +2
In the light of exponentially increasing video content, video summarization has attracted a lot of attention recently due to its ability to optimize time and storage. Characteristi…
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
Rishabh Iyer, John T. Halloran, Kai Wei
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or l…
Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras
Pratik Dubal, Rohan Mahadev, Suraj Kothawade +2
This paper demonstrates the effectiveness of our customized deep learning based video analytics system in various applications focused on security, safety, customer analytics and p…
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks
Vishal Kaushal, Anurag Sahoo, Khoshrav Doctor +5
Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry and pose the challenges of not having adequate computing resources and of h…
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
John Moore, Joel Pfeiffer, Kai Wei +5
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models. This introduces a bias in the training data…