
GBHT: Gradient Boosting Histogram Transform for Density Estimation
In this paper, we propose a density estimation algorithm called Gradient...
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Leveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label lear...
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Gradient Boosted Binary Histogram Ensemble for Largescale Regression
In this paper, we propose a gradient boosting algorithm for largescale ...
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Optimization Induced Equilibrium Networks
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined b...
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PDOeS^2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs
Spherical signals exist in many applications, e.g., planetary data, LiDA...
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Accelerated Gradient Tracking over Timevarying Graphs for Decentralized Optimization
Decentralized optimization over timevarying graphs has been increasingl...
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Graph Contrastive Clustering
Recently, some contrastive learning methods have been proposed to simult...
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PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Aerial Image Segmentation is a particular semantic segmentation problem ...
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Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have attracted more and more attenti...
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Investigating BiLevel Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
BiLevel Optimization (BLO) is originated from the area of economic game...
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Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Most differentiable neural architecture search methods construct a super...
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Learning Optimizationinspired Image Propagation with Control Mechanisms and Architecture Augmentations for Lowlevel Vision
In recent years, building deep learning models from optimization perspec...
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Towards Efficient Scene Understanding via Squeeze Reasoning
Graphbased convolutional model such as nonlocal block has shown to be ...
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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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