
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
Transformer encoding networks have been proved to be a powerful tool of ...
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MultiInterestAware User Modeling for LargeScale Sequential Recommendations
Precise user modeling is critical for online personalized recommendation...
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MultiChannel Sequential Behavior Networks for User Modeling in Online Advertising
Multiple content providers rely on native advertisement for revenue by p...
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Towards Automated Single Channel Source Separation using Neural Networks
Many applications of single channel source separation (SCSS) including a...
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On Learning Sparsely Used Dictionaries from Incomplete Samples
Most existing algorithms for dictionary learning assume that all entries...
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DocTag2Vec: An Embedding Based Multilabel Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection...
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Online Article Ranking as a Constrained, Dynamic, MultiObjective Optimization Problem
The content ranking problem in a social news website, is typically a fun...
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Ranktoengage: New Listwise Approaches to Maximize Engagement
For many internet businesses, presenting a given list of items in an ord...
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RIPML: A Restricted Isometry Property based Approach to Multilabel Learning
The multilabel learning problem with large number of labels, features, a...
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Noisy Inductive Matrix Completion Under Sparse Factor Models
Inductive Matrix Completion (IMC) is an important class of matrix comple...
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Noisy Matrix Completion under Sparse Factor Models
This paper examines a general class of noisy matrix completion tasks whe...
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Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
This work considers an estimation task in compressive sensing, where the...
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On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements
Recent breakthrough results in compressive sensing (CS) have established...
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Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization
Estimating the level set of a signal from measurements is a task that ar...
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Efficient Adaptive Compressive Sensing Using Sparse Hierarchical Learned Dictionaries
Recent breakthrough results in compressed sensing (CS) have established ...
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Akshay Soni
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