
ISTANAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Neural architecture search (NAS) aims to produce the optimal sparse solu...
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Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization
We study stochastic decentralized optimization for the problem of traini...
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PDOeConvs: Partial Differential Operator Based Equivariant Convolutions
Recent research has shown that incorporating equivariance into neural ne...
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Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Existing semantic segmentation approaches either aim to improve the obje...
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MaximumandConcatenation Networks
While successful in many fields, deep neural networks (DNNs) still suffe...
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Classify and Generate Reciprocally: Simultaneous PositiveUnlabelled Learning and Conditional Generation with Extra Data
The scarcity of classlabeled data is a ubiquitous bottleneck in a wide ...
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Invertible Image Rescaling
Highresolution digital images are usually downscaled to fit various dis...
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Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
The convolution operation suffers from a limited receptive filed, while ...
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Revisiting EXTRA for Smooth Distributed Optimization
EXTRA is a popular method for the dencentralized distributed optimizatio...
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Histogram Transform Ensembles for Largescale Regression
We propose a novel algorithm for largescale regression problems named h...
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Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
The correspondence between residual networks and dynamical systems motiv...
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Patchlevel Neighborhood Interpolation: A General and Effective Graphbased Regularization Strategy
Regularization plays a crucial role in machine learning models, especial...
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SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
The panoptic segmentation task requires a unified result from semantic a...
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Tensor QRank: A New Data Dependent Tensor Rank
Recently, the Tensor Nuclear Norm (TNN) regularization based on tSVD ha...
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AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
The design of deep graph models still remains to be investigated and the...
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ExpectationMaximization Attention Networks for Semantic Segmentation
Selfattention mechanism has been widely used for various tasks. It is d...
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ADATucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition
Despite the recent success of deep learning models in numerous applicati...
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Differentiable Linearized ADMM
Recently, a number of learningbased optimization methods that combine d...
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SelfSupervised Convolutional Subspace Clustering Network
Subspace clustering methods based on data selfexpression have become ve...
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Deep Comprehensive Correlation Mining for Image Clustering
Recent developed deep unsupervised methods allow us to jointly learn rep...
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Virtual Adversarial Training on Graph Convolutional Networks in Node Classification
The effectiveness of Graph Convolutional Networks (GCNs) has been demons...
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MultiStage SelfSupervised Learning for Graph Convolutional Networks
Graph Convolutional Networks(GCNs) play a crucial role in graph learning...
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Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN
Deep neural networks have been widely deployed in various machine learni...
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Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors
Most previous works usually explained adversarial examples from several ...
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Sharp Analysis for Nonconvex SGD Escaping from Saddle Points
In this paper, we prove that the simplest Stochastic Gradient Descent (S...
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Lifted Proximal Operator Machines
We propose a new optimization method for training feedforward neural ne...
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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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Optimization Algorithm Inspired Deep Neural Network Structure Design
Deep neural networks have been one of the dominant machine learning appr...
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Joint Subbands Learning with Clique Structures for Wavelet Domain SuperResolution
Convolutional neural networks (CNNs) have recently achieved great succes...
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On the Convergence of Learningbased Iterative Methods for Nonconvex Inverse Problems
Numerous tasks at the core of statistics, learning and vision areas are ...
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Recurrent SqueezeandExcitation Context Aggregation Net for Single Image Deraining
Rain streaks can severely degrade the visibility, which causes many curr...
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Essential Tensor Learning for Multiview Spectral Clustering
Multiview clustering attracts much attention recently, which aims to ta...
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SPIDER: NearOptimal NonConvex Optimization via Stochastic Path Integrated Differential Estimator
In this paper, we propose a new technique named Stochastic PathIntegrat...
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Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements
The recent proposed Tensor Nuclear Norm (TNN) [Lu et al., 2016; 2018a] i...
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Subspace Clustering by Block Diagonal Representation
This paper studies the subspace clustering problem. Given some data poin...
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Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
In this paper, we consider the Tensor Robust Principal Component Analysi...
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Convolutional Neural Networks with Alternately Updated Clique
Improving information flow in deep networks helps to ease the training d...
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Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation
Asynchronous algorithms have attracted much attention recently due to th...
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Alternating Multibit Quantization for Recurrent Neural Networks
Recurrent neural networks have achieved excellent performance in many ap...
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Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis
Spectral Clustering (SC) is a widely used data clustering method which f...
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Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted LowRank Tensors via Convex Optimization
This paper studies the Tensor Robust Principal Component (TRPCA) problem...
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Globally VarianceConstrained Sparse Representation for Image Set Compression
Sparse representation presents an efficient approach to approximately re...
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Graph Construction with Label Information for SemiSupervised Learning
In the literature, most existing graphbased semisupervised learning (S...
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Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
Annotating images with tags is useful for indexing and retrieving images...
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Tensor Sparse and LowRank based Submodule Clustering Method for Multiway Data
A new submodule clustering method via sparse and lowrank representation...
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Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Learning deeper convolutional neural networks becomes a tendency in rece...
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Completing LowRank Matrices with Corrupted Samples from Few Coefficients in General Basis
Subspace recovery from corrupted and missing data is crucial for various...
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Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion
Tagbased image retrieval (TBIR) has drawn much attention in recent year...
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Correntropy Induced L2 Graph for Robust Subspace Clustering
In this paper, we study the robust subspace clustering problem, which ai...
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Correlation Adaptive Subspace Segmentation by Trace Lasso
This paper studies the subspace segmentation problem. Given a set of dat...
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