Search Results for author: Viswanathan Swaminathan

Found 9 papers, 1 papers with code

Privacy Aware Experiments without Cookies

no code implementations3 Nov 2022 Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Viswanathan Swaminathan, Sridhar Mahadevan

We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences.

Experimental Design

Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention

no code implementations18 Jul 2022 Uttaran Bhattacharya, Gang Wu, Stefano Petrangeli, Viswanathan Swaminathan, Dinesh Manocha

We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched.

Highlight Detection

HighlightMe: Detecting Highlights from Human-Centric Videos

no code implementations ICCV 2021 Uttaran Bhattacharya, Gang Wu, Stefano Petrangeli, Viswanathan Swaminathan, Dinesh Manocha

We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames.

Optimal Bidding Strategy without Exploration in Real-time Bidding

no code implementations31 Mar 2020 Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Viswanathan Swaminathan

Earlier works on optimal bidding strategy apply model-based batch reinforcement learning methods which can not generalize to unknown budget and time constraint.


Scalable Bid Landscape Forecasting in Real-time Bidding

no code implementations18 Jan 2020 Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Jason Xie, Gang Wu, Viswanathan Swaminathan

The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price).


Deep Relational Factorization Machines

no code implementations25 Sep 2019 Hongchang Gao, Gang Wu, Ryan Rossi, Viswanathan Swaminathan, Heng Huang

Factorization Machines (FMs) is an important supervised learning approach due to its unique ability to capture feature interactions when dealing with high-dimensional sparse data.

From Thumbnails to Summaries - A single Deep Neural Network to Rule Them All

no code implementations1 Aug 2018 Hongxiang Gu, Viswanathan Swaminathan

The encoder selects a subset from the input video while the decoder seeks to reconstruct the video from the selection.


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