
Large Motion Video SuperResolution with Dual Subnet and MultiStage Communicated Upsampling
Video superresolution (VSR) aims at restoring a video in lowresolution...
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Outlierrobust Kalman Filter in the Presence of Correlated Measurements
We consider the robust filtering problem for a statespace model with ou...
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Differentially Private ADMM Algorithms for Machine Learning
In this paper, we study efficient differentially private alternating dir...
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Boosting Gradient for WhiteBox Adversarial Attacks
Deep neural networks (DNNs) are playing key roles in various artificial ...
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A Single Frame and MultiFrame Joint Network for 360degree Panorama Video SuperResolution
Spherical videos, also known as 360 (panorama) videos, can be viewed wit...
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Video Super Resolution Based on Deep Learning: A comprehensive survey
In recent years, deep learning has made great progress in the fields of ...
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An Unsupervised Deep Learning Method for Parallel Cardiac MRI via TimeInterleaved Sampling
Deep learning has achieved good success in cardiac magnetic resonance im...
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Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Nonconvex Optimization
Largescale nonconvex sparsityconstrained problems have recently gaine...
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Poseadaptive Hierarchical Attention Network for Facial Expression Recognition
Multiview facial expression recognition (FER) is a challenging task bec...
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Paralleltempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling
We propose a new sampler that integrates the protocol of parallel temper...
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Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series
Volatility is a quantity of measurement for the price movements of stock...
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Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
The heavytailed distributions of corrupted outliers and singular values...
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Tractable and Scalable Schatten QuasiNorm Approximations for Rank Minimization
The Schatten quasinorm was introduced to bridge the gap between the tra...
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Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
In this paper, we propose a novel sufficient decrease technique for stoc...
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Thermostatassisted Continuoustempered Hamiltonian Monte Carlo for Multimodal Posterior Sampling
In this paper, we propose a new sampling method named as the thermostat...
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Accelerated Variance Reduced Stochastic ADMM
Recently, many variance reduced stochastic alternating direction method ...
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Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Recently, research on accelerated stochastic gradient descent methods (e...
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Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent
In this paper, we propose a novel sufficient decrease technique for vari...
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Scalable Algorithms for Tractable Schatten QuasiNorm Minimization
The Schattenp quasinorm (0<p<1) is usually used to replace the standar...
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Unified Scalable Equivalent Formulations for Schatten QuasiNorms
The Schatten quasinorm can be used to bridge the gap between the nuclea...
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Structured LowRank Matrix Factorization with Missing and Grossly Corrupted Observations
Recovering lowrank and sparse matrices from incomplete or corrupted obs...
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Yuanyuan Liu
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