
Adaptive Explainable Neural Networks (AxNNs)
While machine learning techniques have been successfully applied in seve...
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Embedding Compression with Isotropic Iterative Quantization
Continuous representation of words is a standard component in deep learn...
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DAGGNN: DAG Structure Learning with Graph Neural Networks
Learning a faithful directed acyclic graph (DAG) from samples of a joint...
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Chart AutoEncoders for Manifold Structured Data
Autoencoding and generative models have made tremendous successes in im...
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Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
This paper presents a variational Bayesian kernel selection (VBKS) algor...
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A Data Driven Approach for Motion Planning of Autonomous Driving Under Complex Scenario
To guarantee the safe and efficient motion planning of autonomous drivin...
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EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Graph representation learning resurges as a trending research subject ow...
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Learning a WeaklySupervised Video ActorAction Segmentation Model with a Wise Selection
We address weaklysupervised video actoraction segmentation (VAAS), whi...
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Policy Gradient from Demonstration and Curiosity
With reinforcement learning, an agent could learn complex behaviors from...
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Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Deep neural networks, while generalize well, are known to be sensitive t...
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DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
Change detection is a basic task of remote sensing image processing. The...
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A laboratorycreated dataset with groundtruth for hyperspectral unmixing evaluation
Spectral unmixing is an important and challenging problem in hyperspectr...
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Adaptively Aligned Image Captioning via Adaptive Attention Time
Recent neural models for image captioning usually employs an encoderdec...
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Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Hierarchical abstractions are a methodology for solving largescale grap...
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CAG: A Realtime Lowcost Enhancedrobustness Hightransferability Contentaware Adversarial Attack Generator
Deep neural networks (DNNs) are vulnerable to adversarial attack despite...
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Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
Remote sensing image scene classification is a fundamental but challengi...
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Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization
Neural networks with ReLU activations have achieved great empirical succ...
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IPC: A Benchmark Data Set for Learning with GraphStructured Data
Benchmark data sets are an indispensable ingredient of the evaluation of...
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Learning Highfidelity Light Field Images From Hybrid Inputs
This paper explores the reconstruction of highfidelity LF images (i.e.,...
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Attention on Attention for Image Captioning
Attention mechanisms are widely used in current encoder/decoder framewor...
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A Survey of Recent Advances in Texture Representation
Texture is a fundamental characteristic of many types of images, and tex...
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SRN: Sideoutput Residual Network for Object Reflection Symmetry Detection and Beyond
In this paper, we establish a baseline for object reflection symmetry de...
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A Sequential Set Generation Method for Predicting SetValued Outputs
Consider a general machine learning setting where the output is a set of...
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Light Field Spatial Superresolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization
Light field (LF) images acquired by handheld devices usually suffer fro...
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ZerothOrder Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications
In this paper, we design and analyze a new zerothorder online algorithm...
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Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
Depth estimation is a fundamental problem for light field photography ap...
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On the Selective and Invariant Representation of DCNN for HighResolution Remote Sensing Image Recognition
Human vision possesses strong invariance in image recognition. The cogni...
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Solving Almost all Systems of Random Quadratic Equations
This paper deals with finding an ndimensional solution x to a system of...
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Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed ...
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RSICB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data
Remote sensing image classification is a fundamental task in remote sens...
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What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Recently, deep convolutional neural network (DCNN) achieved increasingly...
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Solving Largescale Systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow
A novel approach termed stochastic truncated amplitude flow (STAF) is de...
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On Bochner's and Polya's Characterizations of PositiveDefinite Kernels and the Respective Random Feature Maps
Positivedefinite kernel functions are fundamental elements of kernel me...
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SRN: Sideoutput Residual Network for Object Symmetry Detection in the Wild
In this paper, we establish a baseline for object symmetry detection in ...
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Hierarchically Compositional Kernels for Scalable Nonparametric Learning
We propose a novel class of kernels to alleviate the high computational ...
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Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
The problem of modeling and predicting spatiotemporal traffic phenomena ...
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Selflearning Scenespecific Pedestrian Detectors using a Progressive Latent Model
In this paper, a selflearning approach is proposed towards solving scen...
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Light Field Compression with Disparity Guided Sparse Coding based on Structural Key Views
Recent imaging technologies are rapidly evolving for sampling richer and...
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Parallel Gaussian Process Regression for Big Data: LowRank Representation Meets Markov Approximation
The expressive power of a Gaussian process (GP) model comes at a cost of...
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Parallel Gaussian Process Regression with LowRank Covariance Matrix Approximations
Gaussian processes (GP) are Bayesian nonparametric models that are wide...
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GPLocalize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model
Central to robot exploration and mapping is the task of persistent local...
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HEp2 Cell Classification: The Role of Gaussian Scale Space Theory as A Preprocessing Approach
Indirect Immunofluorescence Imaging of Human Epithelial Type 2 (HEp2) c...
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Online dictionary learning for kernel LMS. Analysis and forwardbackward splitting algorithm
Adaptive filtering algorithms operating in reproducing kernel Hilbert sp...
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Symmetric lowrank representation for subspace clustering
We propose a symmetric lowrank representation (SLRR) method for subspac...
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Multitask diffusion adaptation over networks with common latent representations
Online learning with streaming data in a distributed and collaborative m...
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Compressive Phase Retrieval via Reweighted Amplitude Flow
The problem of reconstructing a sparse signal vector from magnitudeonly...
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LinearCost Covariance Functions for Gaussian Random Fields
Gaussian random fields (GRF) are a fundamental stochastic model for spat...
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Transient performance analysis of zeroattracting LMS
Zeroattracting leastmeansquare (ZALMS) algorithm has been widely use...
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FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
The graph convolutional networks (GCN) recently proposed by Kipf and Wel...
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Unsupervised Anomaly Detection via Variational AutoEncoder for Seasonal KPIs in Web Applications
To ensure undisrupted business, large Internet companies need to closely...
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Jie Chen
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City University of Hong Kong
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MIT
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ibm
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University of Oulu
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Université Nice Sophia Antipolis
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IEEE
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Hong Kong Baptist University
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Institute of Computing Technology, Chinese Academy of Sciences
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Nanyang Technological University
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University of Rochester
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Tencent
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Shenzhen University
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Peking University
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Beijing Institute of Technology
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NetEase, Inc
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