
Implicit Maximum Likelihood Estimation
Implicit probabilistic models are models defined naturally in terms of a...
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Diverse Image Synthesis from Semantic Layouts via Conditional IMLE
Most existing methods for conditional image synthesis are only able to g...
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Trajectory Normalized Gradients for Distributed Optimization
Recently, researchers proposed various lowprecision gradient compressio...
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Filter Grafting for Deep Neural Networks
This paper proposes a new learning paradigm called filter grafting, whic...
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Does Preference Always Help? A Holistic Study on PreferenceBased Evolutionary MultiObjective Optimisation Using Reference Points
The ultimate goal of multiobjective optimisation is to help a decision ...
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SearchBased Software Engineering for SelfAdaptive Systems: One Survey, Five Disappointments and Six Opportunities
SearchBased Software Engineering (SBSE) is a promising paradigm that ex...
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Universal Perceptual Grouping
In this work we aim to develop a universal sketch grouper. That is, a gr...
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SemiSupervised Adversarial Monocular Depth Estimation
In this paper, we address the problem of monocular depth estimation when...
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Inclusive GAN: Improving Data and Minority Coverage in Generative Models
Generative Adversarial Networks (GANs) have brought about rapid progress...
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Bayesian Network Based Label Correlation Analysis For Multilabel Classifier Chain
Classifier chain (CC) is a multilabel learning approach that constructs...
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Evolutionary ManyObjective Optimization Based on Adversarial Decomposition
The decompositionbased method has been recognized as a major approach f...
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Integration of Preferences in Decomposition MultiObjective Optimization
Most existing studies on evolutionary multiobjective optimization focus...
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MatchingBased Selection with Incomplete Lists for Decomposition MultiObjective Optimization
The balance between convergence and diversity is a key issue of evolutio...
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Dynamic MultiObjectives Optimization with a Changing Number of Objectives
Existing studies on dynamic multiobjective optimization focus on proble...
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Learning to Optimize Neural Nets
Learning to Optimize is a recently proposed framework for learning optim...
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Fast kNearest Neighbour Search via Prioritized DCI
Most exact methods for knearest neighbour search suffer from the curse ...
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Effective face landmark localization via single deep network
In this paper, we propose a novel face alignment method using single dee...
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Learning to Optimize
Algorithm design is a laborious process and often requires many iteratio...
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Lowdose CT denoising with convolutional neural network
To reduce the potential radiation risk, lowdose CT has attracted much a...
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Fast kNearest Neighbour Search via Dynamic Continuous Indexing
Existing methods for retrieving knearest neighbours suffer from the cur...
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Bandit Label Inference for Weakly Supervised Learning
The scarcity of data annotated at the desired level of granularity is a ...
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Iterative Instance Segmentation
Existing methods for pixelwise labelling tasks generally disregard the ...
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TwoArchive Evolutionary Algorithm for Constrained MultiObjective Optimization
When solving constrained multiobjective optimization problems, an impor...
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Interactive Decomposition MultiObjective Optimization via Progressively Learned Value Functions
Decomposition has become an increasingly popular technique for evolution...
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Robust Group Comparison Using NonParametric BlockBased Statistics
Voxelbased analysis methods localize brain structural differences by pe...
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On the Implicit Assumptions of GANs
Generative adversarial nets (GANs) have generated a lot of excitement. D...
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Are All Training Examples Created Equal? An Empirical Study
Modern computer vision algorithms often rely on very large training data...
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Speaker Adaptation for EndtoEnd CTC Models
We propose two approaches for speaker adaptation in endtoend (E2E) aut...
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Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
It is not uncommon that metaheuristic algorithms contain some intrinsic...
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Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons
Visualisation is an effective way to facilitate the analysis and underst...
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SuperResolution via Conditional Implicit Maximum Likelihood Estimation
Singleimage superresolution (SISR) is a canonical problem with diverse...
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Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark
Substantial efforts have been devoted more recently to presenting variou...
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A hierarchical neural hybrid method for failure probability estimation
Failure probability evaluation for complex physical and engineering syst...
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The Mass, Fake News, and Cognition Security
The wide spread of fake news in social networks is posing threats to soc...
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D3M: A deep domain decomposition method for partial differential equations
A stateoftheart deep domain decomposition method (D3M) based on the v...
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Model Adaption Object Detection System for Robot
How to detect the object and guide the robot to get close to the object ...
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Asymmetric CoTeaching for Unsupervised Cross Domain Person ReIdentification
Person reidentification (reID), is a challenging task due to the high ...
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sPortfolio: Stratified Visual Analysis of Stock Portfolios
Quantitative Investment, built on the solid foundation of robust financi...
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Covert Communication in ContinuousTime Systems
Recent works have considered the ability of transmitter Alice to communi...
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RoutingLed Placement of VNFs in Arbitrary Networks
The ever increasing demand for computing resources has led to the creati...
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MultiModal Graph Neural Network for Joint Reasoning on Vision and Scene Text
Answering questions that require reading texts in an image is challengin...
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Architecture Disentanglement for Deep Neural Networks
Deep Neural Networks (DNNs) are central to deep learning, and understand...
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Surrogate Assisted Evolutionary Algorithm for Medium Scale Expensive MultiObjective Optimisation Problems
Building a surrogate model of an objective function has shown to be effe...
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Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study
Datadriven defect prediction has become increasingly important in softw...
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Ke Li
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Ph.D., Lecturer(Assistant Professor) in Data Analytics at University of Exeter College of Engineering, Mathematics and Physical Sciences since 2016, EPSRC Research Fellow, School of Computer Science, University of Birmingham, Oct. 2015∼Nov. 2016, Postdoctoral Research Associate, Department of Electrical and Computer Engineering, Michigan State University, Sep. 2014∼Aug. 2015, Research Associate, Department of Computer Science, City University of Hong Kong, Aug. 2013∼Jul. 2014, Visiting Scholar, Department of Electrical and Computer Engineering, Michigan State University, Dec. 2013∼Apr. 2014