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Learning Structral coherence Via Generative Adversarial Network for Single Image Super-Resolution
Among the major remaining challenges for single image super resolution (...
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Generating a Doppelganger Graph: Resembling but Distinct
Deep generative models, since their inception, have become increasingly ...
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Directed Acyclic Graph Neural Networks
Graph-structured data ubiquitously appears in science and engineering. G...
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Discrete Graph Structure Learning for Forecasting Multiple Time Series
Time series forecasting is an extensively studied subject in statistics,...
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Handling Noisy Labels via One-Step Abductive Multi-Target Learning
Learning from noisy labels is an important concern because of the lack o...
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Edge Adaptive Hybrid Regularization Model For Image Deblurring
A spatially fixed parameter of regularization item for whole images does...
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A Bounded Multi-Vacation Queue Model for Multi-stage Sleep Control 5G Base station
Modelling and control of energy consumption is an important problem in t...
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Deep reinforcement learning for RAN optimization and control
Due to the high variability of the traffic in the radio access network (...
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Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors
Nowadays, nonnegative matrix factorization (NMF) based methods have been...
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Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution
Light field (LF) images acquired by hand-held devices usually suffer fro...
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The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims toencourage research ...
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Distributed Linear Equations over Random Networks
Distributed linear algebraic equation over networks, where nodes hold a ...
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Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting,...
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Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
The linear inverse problem is fundamental to the development of various ...
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Generalized multiscale approximation of a multipoint flux mixed finite element method for Darcy-Forchheimer model
In this paper, we propose a multiscale method for the Darcy-Forchheimer ...
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Adaptive generalized multiscale approximation of a mixed finite element method with velocity elimination
In this paper, we propose offline and online adaptive enrichment algorit...
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Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning
This paper explores the problem of hyperspectral image (HSI) super-resol...
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Policy Gradient from Demonstration and Curiosity
With reinforcement learning, an agent could learn complex behaviors from...
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AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
Domain adaptive person re-identification (re-ID) is a challenging task, ...
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Adaptive Explainable Neural Networks (AxNNs)
While machine learning techniques have been successfully applied in seve...
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Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization
Light field (LF) images acquired by hand-held devices usually suffer fro...
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Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection
We address weakly-supervised video actor-action segmentation (VAAS), whi...
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Automated discovery of a robust interatomic potential for aluminum
Atomistic molecular dynamics simulation is an important tool for predict...
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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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Asynchronous parallel adaptive stochastic gradient methods
Stochastic gradient methods (SGMs) are the predominant approaches to tra...
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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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Linearly Convergent Algorithm with Variance Reduction for Distributed Stochastic Optimization
This paper considers a distributed stochastic strongly convex optimizati...
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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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Embedding Compression with Isotropic Iterative Quantization
Continuous representation of words is a standard component in deep learn...
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Multitask learning over graphs
The problem of learning simultaneously several related tasks has receive...
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Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Hierarchical abstractions are a methodology for solving large-scale grap...
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Chart Auto-Encoders for Manifold Structured Data
Auto-encoding and generative models have made tremendous successes in im...
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A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
Paper-reviewer recommendation task is of significant academic importance...
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CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator
Deep neural networks (DNNs) are vulnerable to adversarial attack despite...
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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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DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition
Self-attention networks (SAN) have been introduced into automatic speech...
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Adaptively Aligned Image Captioning via Adaptive Attention Time
Recent neural models for image captioning usually employs an encoder-dec...
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Flexible, Fast and Accurate Densely-Sampled Light Field Reconstruction Network
The densely-sampled light field (LF) is highly desirable in various appl...
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Attention on Attention for Image Captioning
Attention mechanisms are widely used in current encoder/decoder framewor...
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Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
Anti-money laundering (AML) regulations play a critical role in safeguar...
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Learning High-fidelity Light Field Images From Hybrid Inputs
This paper explores the reconstruction of high-fidelity LF images (i.e.,...
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Distributed Global Output-Feedback Control for a Class of Euler-Lagrange Systems
This published paper investigates the distributed tracking control probl...
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Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart
Large intelligent surface/antennas (LISA), a two-dimensional artificial ...
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Learning discriminative features in sequence training without requiring framewise labelled data
In this work, we try to answer two questions: Can deeply learned feature...
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IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of...
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Time Series Simulation by Conditional Generative Adversarial Net
Generative Adversarial Net (GAN) has been proven to be a powerful machin...
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DAG-GNN: DAG Structure Learning with Graph Neural Networks
Learning a faithful directed acyclic graph (DAG) from samples of a joint...
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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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A Sequential Set Generation Method for Predicting Set-Valued Outputs
Consider a general machine learning setting where the output is a set of...
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Stratified Labeling for Surface Consistent Parallax Correction and Occlusion Completion
The light field faithfully records the spatial and angular configuration...
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