
Outlier Detection and Data Clustering via Innovation Search
The idea of Innovation Search was proposed as a data clustering method i...
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Graph Analysis and Graph Pooling in the Spatial Domain
The spatial convolution layer which is widely used in the Graph Neural N...
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CycleSUM: Cycleconsistent Adversarial LSTM Networks for Unsupervised Video Summarization
In this paper, we present a novel unsupervised video summarization model...
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A Fourier Analytical Approach to Estimation of Smooth Functions in Gaussian Shift Model
We study the estimation of f() under Gaussian shift model = +, where ∈^d...
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Selective Convolutional Network: An Efficient Object Detector with Ignoring Background
It is well known that attention mechanisms can effectively improve the p...
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Optimistic Adaptive Acceleration for Optimization
We consider a new variant of AMSGrad. AMSGrad RKK18 is a popular adaptiv...
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StructureFeature based Graph Selfadaptive Pooling
Various methods to deal with graph data have been proposed in recent yea...
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Logician: A Unified EndtoEnd Neural Approach for OpenDomain Information Extraction
In this paper, we consider the problem of open information extraction (O...
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RGBD SLAM in Dynamic Environments Using Points Correlations
This paper proposed a novel RGBD SLAM method for dynamic environments. ...
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On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
The DANE algorithm is an approximate Newton method popularly used for co...
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MultiSpectral Visual Odometry without Explicit Stereo Matching
Multispectral sensors consisting of a standard (visiblelight) camera a...
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A TwoStage Approach to Multivariate Linear Regression with Sparsely Mismatched Data
A tacit assumption in linear regression is that (response, predictor)pa...
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Permutation Recovery from Multiple Measurement Vectors in Unlabeled Sensing
In "Unlabeled Sensing", one observes a set of linear measurements of an ...
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Compressed Counting
Counting is among the most fundamental operations in computing. For exam...
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Image matting with normalized weight and semisupervised learning
Image matting is an important vision problem. The main stream methods fo...
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Tunable GMM Kernels
The recently proposed "generalized minmax" (GMM) kernel can be efficien...
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Generalized Intersection Kernel
Following the very recent line of work on the "generalized minmax" (GMM...
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L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction
Current face alignment algorithms can robustly find a set of landmarks a...
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Nystrom Method for Approximating the GMM Kernel
The GMM (generalized minmax) kernel was recently proposed (Li, 2016) as...
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Linearized GMM Kernels and Normalized Random Fourier Features
The method of "random Fourier features (RFF)" has become a popular tool ...
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A Tight Bound of Hard Thresholding
This paper is concerned with the hard thresholding technique which sets ...
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A Comparison Study of Nonlinear Kernels
In this paper, we compare 5 different nonlinear kernels: minmax, RBF, f...
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Constrained LowRank Learning Using Least SquaresBased Regularization
Lowrank learning has attracted much attention recently due to its effic...
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2Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search
The method of random projections has become a standard tool for machine ...
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Sign Stable Random Projections for LargeScale Learning
We study the use of "sign αstable random projections" (where 0<α≤ 2) fo...
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Regularizationfree estimation in trace regression with symmetric positive semidefinite matrices
Over the past few years, trace regression models have received considera...
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Efficient Online Minimization for LowRank Subspace Clustering
Lowrank representation (LRR) has been a significant method for segmenti...
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MinMax Kernels
The minmax kernel is a generalization of the popular resemblance kernel...
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Asymmetric Minwise Hashing
Minwise hashing (Minhash) is a widely popular indexing scheme in practic...
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Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)
Recently it was shown that the problem of Maximum Inner Product Search (...
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In Defense of MinHash Over SimHash
MinHash and SimHash are the two widely adopted Locality Sensitive Hashin...
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Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
We present the first provably sublinear time algorithm for approximate M...
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CoRE Kernels
The term "CoRE kernel" stands for correlationresemblance kernel. In man...
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Graph Kernels via Functional Embedding
We propose a representation of graph as a functional object derived from...
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A New Space for Comparing Graphs
Finding a new mathematical representations for graph, which allows direc...
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Multilabel ensemble based on variable pairwise constraint projection
Multilabel classification has attracted an increasing amount of attenti...
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Adaptive Stochastic Alternating Direction Method of Multipliers
The Alternating Direction Method of Multipliers (ADMM) has been studied ...
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Gradient Hard Thresholding Pursuit for SparsityConstrained Optimization
Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedu...
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Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics
We investigate a generic problem of learning pairwise exponential family...
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Exact Sparse Recovery with L0 Projections
Many applications concern sparse signals, for example, detecting anomali...
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ABCLogitBoost for Multiclass Classification
We develop abclogitboost, based on the prior work on abcboost and robu...
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Object Proposal with Kernelized Partial Ranking
Object proposals are an ensemble of bounding boxes with high potential t...
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One Permutation Hashing for Efficient Search and Learning
Recently, the method of bbit minwise hashing has been applied to large...
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Improving Compressed Counting
Compressed Counting (CC) [22] was recently proposed for estimating the a...
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Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost
Logitboost is an influential boosting algorithm for classification. In t...
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Approximating HigherOrder Distances Using Random Projections
We provide a simple method and relevant theoretical analysis for efficie...
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Training Logistic Regression and SVM on 200GB Data Using bBit Minwise Hashing and Comparisons with Vowpal Wabbit (VW)
We generated a dataset of 200 GB with 10^9 features, to test our recent ...
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Accurate Estimators for Improving Minwise Hashing and bBit Minwise Hashing
Minwise hashing is the standard technique in the context of search and d...
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Hashing Algorithms for LargeScale Learning
In this paper, we first demonstrate that bbit minwise hashing, whose es...
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bBit Minwise Hashing for LargeScale Linear SVM
In this paper, we propose to (seamlessly) integrate bbit minwise hashin...
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