
Mechanical Cloak via DataDriven Aperiodic Metamaterial Design
Mechanical cloaks are materials engineered to manipulate the elastic res...
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Causally Invariant Predictor with ShiftRobustness
This paper proposes an invariant causal predictor that is robust to dist...
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Regularized OFU: an Efficient UCB Estimator forNonlinear Contextual Bandit
Balancing exploration and exploitation (EE) is a fundamental problem in ...
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RDrop: Regularized Dropout for Neural Networks
Dropout is a powerful and widely used technique to regularize the traini...
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Scalable Gaussian Processes for DataDriven Design using Big Data with Categorical Factors
Scientific and engineering problems often require the use of artificial ...
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Exploiting Negative Learning for Implicit Pseudo Label Rectification in SourceFree Domain Adaptive Semantic Segmentation
It is desirable to transfer the knowledge stored in a welltrained sourc...
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Large Scale Private Learning via Lowrank Reparametrization
We propose a reparametrization scheme to address the challenges of apply...
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Enhanced Hyperspectral Image SuperResolution via RGB Fusion and TVTV Minimization
Hyperspectral (HS) images contain detailed spectral information that has...
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DataDriven Multiscale Design of Cellular Composites with Multiclass Microstructures for Natural Frequency Maximization
For natural frequency optimization of engineering structures, cellular c...
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PriorGrad: Improving Conditional Denoising Diffusion Models with DataDriven Adaptive Prior
Denoising diffusion probabilistic models have been recently proposed to ...
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Multilayered Network Exploration via Random Walks: From Offline Optimization to Online Learning
Multilayered network exploration (MuLaNE) problem is an important probl...
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Incorporating NODE with Pretrained Neural Differential Operator for Learning Dynamics
Learning dynamics governed by differential equations is crucial for pred...
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PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Engineering design tasks often require synthesizing new designs that mee...
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Network Inference and Influence Maximization from Samples
Influence maximization is the task of selecting a small number of seed n...
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DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on WholeBody MRI
Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposi...
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PriorEnhanced FewShot Segmentation with MetaPrototypes
Fewshot segmentation (FSS) performance has been extensively promoted by...
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MachineLearning NonConservative Dynamics for NewPhysics Detection
Energy conservation is a basic physics principle, the breakdown of which...
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Adversarial Training with Rectified Rejection
Adversarial training (AT) is one of the most effective strategies for pr...
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Learning a ModelDriven Variational Network for Deformable Image Registration
Datadriven deep learning approaches to image registration can be less a...
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CoMAE: A Multifactor Hierarchical Framework for Empathetic Response Generation
The capacity of empathy is crucial to the success of opendomain dialog ...
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A Graph Neural Network Approach for Product Relationship Prediction
Graph Neural Networks have revolutionized many machine learning tasks in...
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OvertheAir Computation via Reconfigurable Intelligent Surface
Overtheair computation (AirComp) is a disruptive technique for fast wi...
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Pure Exploration Bandit Problem with General Reward Functions Depending on Full Distributions
In this paper, we study the pure exploration bandit model on general dis...
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Integrating Information Theory and Adversarial Learning for Crossmodal Retrieval
Accurately matching visual and textual data in crossmodal retrieval has...
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DartsConformer: Towards Efficient GradientBased Neural Architecture Search For EndtoEnd ASR
Neural architecture search (NAS) has been successfully applied to tasks ...
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FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
We consider the problem of estimating a particular type of linear nonGa...
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Lifelong Person ReIdentification via Adaptive Knowledge Accumulation
Person ReID methods always learn through a stationary domain that is fix...
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A Novel Probability Weighting Method To Fit Gaussian Functions
Gaussian functions are commonly used in different fields, many real sign...
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FSNet: Fast Shapebased Network for CategoryLevel 6D Object Pose Estimation with Decoupled Rotation Mechanism
In this paper, we focus on categorylevel 6D pose and size estimation fr...
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Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
Convolutional neural network training can suffer from diverse issues lik...
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PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
Computational Fluid Dynamics (CFD) simulations are a very important tool...
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RangeGAN: RangeConstrained Generative Adversarial Network for Conditioned Design Synthesis
Typical engineering design tasks require the effort to modify designs it...
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IHGAN: A Conditional Generative Model for Implicit SurfaceBased Inverse Design of Cellular Structures
Variabledensity cellular structures can overcome connectivity and manuf...
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Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
The privacy leakage of the model about the training data can be bounded ...
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Combinatorial Pure Exploration with Bottleneck Reward Function and its Extension to General Reward Functions
In this paper, we study the Combinatorial Pure Exploration problem with ...
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A Generative Approach to Joint Modeling of Quantitative and Qualitative Responses
In many scientific areas, data with quantitative and qualitative (QQ) re...
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MultiScale Cost Volumes Cascade Network for Stereo Matching
Stereo matching is essential for robot navigation. However, the accuracy...
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Deep Image Retrieval: A Survey
In recent years a vast amount of visual content has been generated and s...
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UAVAssisted OvertheAir Computation
Overtheair computation (AirComp) provides a promising way to support u...
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BNinvariant sharpness regularizes the training model to better generalization
It is arguably believed that flatter minima can generalize better. Howev...
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Towards Accelerating Training of Batch Normalization: A Manifold Perspective
Batch normalization (BN) has become a crucial component across diverse d...
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Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation
In aerodynamic shape optimization, the convergence and computational cos...
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FDMT: A Benchmark Dataset for Finegrained Domain Adaptation in Machine Translation
Previous domain adaptation research usually neglect the diversity in tra...
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Time Series Domain Adaptation via Sparse Associative Structure Alignment
Domain adaptation on time series data is an important but challenging ta...
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Nonstationarity Analysis of Materials Microstructures via Fisher Score Vectors
Microstructures are critical to the physical properties of materials. St...
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Identifying Invariant Texture Violation for Robust Deepfake Detection
Existing deepfake detection methods have reported promising indistribut...
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The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
Despite their overwhelming capacity to overfit, deep neural networks tra...
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Multitask machine learning of collective variables for enhanced sampling of rare events
Computing accurate reaction rates is a central challenge in computationa...
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Maximizing Social Welfare in a Competitive Diffusion Model
Influence maximization (IM) has garnered a lot of attention in the liter...
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Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules
Recent research on joint source channel coding (JSCC) for wireless commu...
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Wei Chen
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Senior Researcher at Microsoft Research Asia, Adjunct Professor in the Institute of Interdisciplinary Information Sciences, Tsinghua University, Adjunct Researcher in the Institute of Computing Technology, Chinese Academy of Sciences, Ph.D degree from the Department of Computer Science, Cornell University, Scientist at Oracle Corporation 2004.