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20162024
most citedCOVID-19 Time-series Prediction by Joint Dictionary Learning and Online NMF

3 citations · 11 across the 23 of their papers we have counts for

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Showing 2019Show all

13 papers · 1 filter

cs.CL2019

Topic-aware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization

Yuchen Guo, Nicholas Hanoian, Zhexiao Lin +8

We propose a novel model for a topic-aware chatbot by combining the traditional Recurrent Neural Network (RNN) encoder-decoder model with a topic attention layer based on Nonnegati…

math.NA2019

Sketching for Motzkin's Iterative Method for Linear Systems

Elizaveta Rebrova, Deanna Needell

Projection-based iterative methods for solving large over-determined linear systems are well-known for their simplicity and computational efficiency. It is also known that the corr…

cs.LG2019

Online matrix factorization for Markovian data and applications to Network Dictionary Learning

Hanbaek Lyu, Deanna Needell, Laura Balzano

Online Matrix Factorization (OMF) is a fundamental tool for dictionary learning problems, giving an approximate representation of complex data sets in terms of a reduced number of…

cs.IT2019

Weighted matrix completion from non-random, non-uniform sampling patterns

Simon Foucart, Deanna Needell, Reese Pathak +2

We study the matrix completion problem when the observation pattern is deterministic and possibly non-uniform. We propose a simple and efficient debiased projection scheme for reco…

math.NA2019

Adaptive Sketch-and-Project Methods for Solving Linear Systems

Robert Gower, Denali Molitor, Jacob Moorman +1

We present new adaptive sampling rules for the sketch-and-project method for solving linear systems. To deduce our new sampling rules, we first show how the progress of one step of…

math.NA2019

Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery

Rachel Grotheer, Shuang Li, Anna Ma +2

Low-rank tensor recovery problems have been widely studied in many applications of signal processing and machine learning. Tucker decomposition is known as one of the most popular…