Payal Bajaj

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YouTube draws large number of users who contribute actively by uploading videos or commenting on existing videos. However, being a crowd sourced and large content pushed onto it, there is limited control over the content. This makes malicious users push content (videos and comments) which is inappropriate (unsafe), particularly when such content is placed(More)
The study of artificial intelligence can be simplified into one goal: trying to mimic/enhance human senses. This paper attempts to combine computer vision and natural language processing to create a question answer system. This system takes a question and an image as input and outputs a response to the answer based on how the RCNN understands the question(More)
Online TV has seen rapid growth in recent years, with most of the large media companies broadcasting their linear content online. Access to the online TV accounts is protected by an authentication, and like the traditional cable TV subscription, users in the same household share the online TV credentials. However, as the standard data collection techniques(More)
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects training label quality, but is difficult to learn without any ground truth labels. We instead(More)
A fluorescein-based fluorescent probe has been designed and synthesised that selectively detects H2 S in aqueous medium, among various analytes tested. This fluorescein-based fluorescent probe has also been successfully utilised for real-time imaging of exo- and endogenously produced H2 S in cancer cells and normal cells. Moreover, the probe can also detect(More)
Popularity of online videos is increasing at a rapid rate. Not only the users can access these videos online, but they can also upload video content on platforms like YouTube and Myspace. These videos are indexed by user generated multimedia annotation, also known as metadata, which is usually rich contextual information added by users about the content of(More)
Customers may interact with a retail store through many channels. Technology now makes it is possible to track customer behavior across channels. We propose a system where items are recommended based on learning channel specific similarities between customers and items. This is done by treating recommendations as a learning to rank problem and minimizing(More)
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