
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
It has been shown that equivariant convolution is very helpful for many ...
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A Unified HyperGAN Model for Unpaired Multicontrast MR Image Translation
Crosscontrast image translation is an important task for completing mis...
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Learning an Explicit Hyperparameter Prediction Policy Conditioned on Tasks
Meta learning has attracted much attention recently in machine learning ...
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Training Networks in Null Space of Feature Covariance for Continual Learning
In the setting of continual learning, a network is trained on a sequence...
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Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
Differential evolution is one of the most prestigious populationbased s...
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Robust spectral compressive sensing via vanilla gradient descent
This paper investigates robust recovery of an undamped or damped spectra...
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Amortized Variational Deep Q Network
Efficient exploration is one of the most important issues in deep reinfo...
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SelectProtoNet: Learning to Select for FewShot Disease Subtype Prediction
Current machine learning has made great progress on computer vision and ...
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MetaLRScheduleNet: Learned LR Schedules that Scale and Generalize
The learning rate (LR) is one of the most important hyperparameters in ...
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CrossAttention in Coupled Unmixing Nets for Unsupervised Hyperspectral SuperResolution
The recent advancement of deep learning techniques has made great progre...
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Graph Neural Network Encoding for Community Detection in Attribute Networks
In this paper, we first propose a graph neural network encoding method f...
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Learning to be Global Optimizer
The advancement of artificial intelligence has cast a new light on the d...
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On Hyperparameter Tuning for Stochastic Optimization Algorithms
This paper proposes the firstever algorithmic framework for tuning hype...
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Adaptive Structural HyperParameter Configuration by QLearning
Tuning hyperparameters for evolutionary algorithms is an important issu...
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Learning Adaptive Loss for Robust Learning with Noisy Labels
Robust loss minimization is an important strategy for handling robust le...
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Variational Bayes' method for functions with applications to some inverse problems
Bayesian approach as a useful tool for quantifying uncertainties has bee...
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Push the Student to Learn Right: Progressive Gradient Correcting by Metalearner on Corrupted Labels
While deep networks have strong fitting capability to complex input patt...
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Tug the Student to Learn Right: Progressive Gradient Correcting by Metalearner on Corrupted Labels
While deep networks have strong fitting capability to complex input patt...
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Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of ...
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HyperAdam: A Learnable TaskAdaptive Adam for Network Training
Deep neural networks are traditionally trained using humandesigned stoc...
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ModelDriven Deep Learning for Physical Layer Communications
Intelligent communication is gradually considered as the mainstream dire...
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Unpaired Brain MRtoCT Synthesis using a StructureConstrained CycleGAN
The cycleGAN is becoming an influential method in medical image synthesi...
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Small Sample Learning in Big Data Era
As a promising area in artificial intelligence, a new learning paradigm,...
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Semisupervised CNN for Single Image Rain Removal
Single image rain removal is a typical inverse problem in computer visio...
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Joint Analysis of Individuallevel and Summarylevel GWAS Data by Leveraging Pleiotropy
A large number of recent genomewide association studies (GWASs) for com...
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Learning through deterministic assignment of hidden parameters
Supervised learning frequently boils down to determining hidden and brig...
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Neural MultiAtlas Label Fusion: Application to Cardiac MR Images
Multiatlas segmentation approach is one of the most widelyused image s...
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SPLBoost: An Improved Robust Boosting Algorithm Based on Selfpaced Learning
It is known that Boosting can be interpreted as a gradient descent techn...
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ADMMNet: A Deep Learning Approach for Compressive Sensing MRI
Compressive sensing (CS) is an effective approach for fast Magnetic Reso...
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Denoising Hyperspectral Image with Noni.i.d. Noise Structure
Hyperspectral image (HSI) denoising has been attracting much research at...
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Lowrank Matrix Factorization under General Mixture Noise Distributions
Many computer vision problems can be posed as learning a lowdimensional...
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Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition
In this paper, we present a novel approach to automatic 3D Facial Expres...
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Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in...
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Learning a Convolutional Neural Network for Nonuniform Motion Blur Removal
In this paper, we address the problem of estimating and removing nonuni...
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Video Primal Sketch: A Unified MiddleLevel Representation for Video
This paper presents a middlelevel video representation named Video Prim...
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On the Optimal Solution of Weighted Nuclear Norm Minimization
In recent years, the nuclear norm minimization (NNM) problem has been at...
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Sparse KMeans with ℓ_∞/ℓ_0 Penalty for HighDimensional Data Clustering
Sparse clustering, which aims to find a proper partition of an extremely...
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Learning rates of l^q coefficient regularization learning with Gaussian kernel
Regularization is a well recognized powerful strategy to improve the per...
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Compressed Sensing SAR Imaging with Multilook Processing
Multilook processing is a widely used speckle reduction approach in synt...
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A recursive divideandconquer approach for sparse principal component analysis
In this paper, a new method is proposed for sparse PCA based on the recu...
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Zongben Xu
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