
Adaptive Multilevel Hypergradient Descent
Adaptive learning rates can lead to faster convergence and better final ...
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Riemannian stochastic recursive momentum method for nonconvex optimization
We propose a stochastic recursive momentum method for Riemannian noncon...
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Regularized Flexible Activation Function Combinations for Deep Neural Networks
Activation in deep neural networks is fundamental to achieving nonlinea...
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Graph Neural Networks with Haar TransformBased Convolution and Pooling: A Complete Guide
Graph Neural Networks (GNNs) have recently caught great attention and ac...
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Variance reduction for Riemannian nonconvex optimization with batch size adaptation
Variance reduction techniques are popular in accelerating gradient desce...
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A Bayesian Long ShortTerm Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
ValueatRisk (VaR) and Expected Shortfall (ES) are widely used in the f...
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ADAMT: A Stochastic Optimization with Trend Correction Scheme
Adamtype optimizers, as a class of adaptive moment estimation methods w...
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Coupling Matrix Manifolds and Their Applications in Optimal Transport
Optimal transport (OT) is a powerful tool for measuring the distance bet...
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Efficient Spatial Nearest Neighbor Queries Based on Multilayer Voronoi Diagrams
Nearest neighbor (NN) problem is an important scientific problem. The NN...
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A Heuristic Algorithm Based on Tour Rebuilding Operator for the Traveling Salesman Problem
TSP (Traveling Salesman Problem), a classic NPcomplete problem in combi...
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LSTMAssisted Evolutionary SelfExpressive Subspace Clustering
Massive volumes of highdimensional data that evolves over time is conti...
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TensorTrain Parameterization for Ultra Dimensionality Reduction
Locality preserving projections (LPP) are a classical dimensionality red...
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Shared Generative Latent Representation Learning for Multiview Clustering
Clustering multiview data has been a fundamental research topic in the ...
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DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets
Smartphones have become the ultimate 'personal' computer, yet despite th...
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Manifold Optimisation Assisted Gaussian Variational Approximation
Variational approximation methods are a way to approximate the posterior...
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Sparse Least Squares Low Rank Kernel Machines
A general framework of least squares support vector machine with low ran...
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A Review for Weighted MinHash Algorithms
Data similarity (or distance) computation is a fundamental research topi...
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WhereandWhen to Look: Deep Siamese Attention Networks for Videobased Person Reidentification
Videobased person reidentification (reid) is a central application in...
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Deep Coattention based Comparators For Relative Representation Learning in Person Reidentification
Person reidentification (reID) requires rapid, flexible yet discrimina...
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Tensorial Recurrent Neural Networks for Longitudinal Data Analysis
Traditional Recurrent Neural Networks assume vectorized data as inputs. ...
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WhatandWhere to Match: Deep Spatially Multiplicative Integration Networks for Person Reidentification
Matching pedestrians across disjoint camera views, known as person reid...
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Vectorial Dimension Reduction for Tensors Based on Bayesian Inference
Dimensionality reduction for highorder tensors is a challenging problem...
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Deep Adaptive Feature Embedding with Local Sample Distributions for Person Reidentification
Person reidentification (reid) aims to match pedestrians observed by d...
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Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem
In retailer management, the Newsvendor problem has widely attracted atte...
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Localized LRR on Grassmann Manifolds: An Extrinsic View
Subspace data representation has recently become a common practice in ma...
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Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Partial least squares regression (PLSR) has been a popular technique to ...
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Matrix Variate RBM Model with Gaussian Distributions
Restricted Boltzmann Machine (RBM) is a particular type of random neural...
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Lowrank Multiview Clustering in ThirdOrder Tensor Space
The plenty information from multiple views data as well as the complemen...
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Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in MultiCamera Video Surveillance
In multicamera video surveillance, it is challenging to represent video...
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Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Due to its promising classification performance, sparse representation b...
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Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds
As a significant subspace clustering method, low rank representation (LR...
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Kernelized LRR on Grassmann Manifolds for Subspace Clustering
Low rank representation (LRR) has recently attracted great interest due ...
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BlockDiagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition
In a sparse representation based recognition scheme, it is critical to l...
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Mixture of BilateralProjection Twodimensional Probabilistic Principal Component Analysis
The probabilistic principal component analysis (PPCA) is built upon a gl...
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LowRank Representation over the Manifold of Curves
In machine learning it is common to interpret each data point as a vecto...
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Matrix Variate RBM and Its Applications
Restricted Boltzmann Machine (RBM) is an importan t generative model mo...
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Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Sparse subspace clustering (SSC), as one of the most successful subspace...
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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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Fast Optimization Algorithm on Riemannian Manifolds and Its Application in LowRank Representation
The paper addresses the problem of optimizing a class of composite funct...
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l1norm Penalized Orthogonal Forward Regression
A l1norm penalized orthogonal forward regression (l1POFR) algorithm is...
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Segmentation of Subspaces in Sequential Data
We propose Ordered Subspace Clustering (OSC) to segment data drawn from ...
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Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
Many computer vision algorithms employ subspace models to represent data...
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Kernelized Low Rank Representation on Grassmann Manifolds
Low rank representation (LRR) has recently attracted great interest due ...
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Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Tensors or multiarray data are generalizations of matrices. Tensor clust...
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Junbin Gao
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Professor of Big Data Analytics at The University of Sydney Business School