Search Results for author: Yi Wang

Found 81 papers, 22 papers with code

Image Synthesis via Semantic Composition

no code implementations ICCV 2021 Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia

In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts.

Image Generation Semantic Composition

Conditional Temporal Variational AutoEncoder for Action Video Prediction

no code implementations12 Aug 2021 Xiaogang Xu, Yi Wang, LiWei Wang, Bei Yu, Jiaya Jia

To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video.

motion prediction Video Prediction

Open-World Entity Segmentation

2 code implementations29 Jul 2021 Lu Qi, Jason Kuen, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, Jiaya Jia

We introduce a new image segmentation task, termed Entity Segmentation (ES) with the aim to segment all visual entities in an image without considering semantic category labels.

Image Manipulation Semantic Segmentation

Looking Twice for Partial Clues: Weakly-supervised Part-Mentored Attention Network for Vehicle Re-Identification

no code implementations17 Jul 2021 Lisha Tang, Yi Wang, Lap-Pui Chau

In this paper, we propose a weakly supervised Part-Mentored Attention Network (PMANet) composed of a Part Attention Network (PANet) for vehicle part localization with self-attention and a Part-Mentored Network (PMNet) for mentoring the global and local feature aggregation.

Vehicle Re-Identification

Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach

no code implementations16 Jul 2021 Yang Li, Jiazheng Li, Yi Wang

Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables.

Decision Making Federated Learning

Cost-Oriented Load Forecasting

no code implementations5 Jul 2021 Jialun Zhang, Yi Wang, Gabriela Hug

Accurate load prediction is an effective way to reduce power system operation costs.

Load Forecasting

FedNILM: Applying Federated Learning to NILM Applications at the Edge

no code implementations7 Jun 2021 Yu Zhang, Guoming Tang, Qianyi Huang, Yi Wang, Xudong Wang, Jiadong Lou

Non-intrusive load monitoring (NILM) helps disaggregate the household's main electricity consumption to energy usages of individual appliances, thus greatly cutting down the cost in fine-grained household load monitoring.

Federated Learning Model Compression +2

More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring

no code implementations1 Jun 2021 Yu Zhang, Guoming Tang, Qianyi Huang, Yi Wang, Hong Xu

Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances.

Non-Intrusive Load Monitoring

Multi-object Tracking with Tracked Object Bounding Box Association

1 code implementation17 May 2021 Nanyang Yang, Yi Wang, Lap-Pui Chau

The CenterTrack tracking algorithm achieves state-of-the-art tracking performance using a simple detection model and single-frame spatial offsets to localize objects and predict their associations in a single network.

Multi-Object Tracking

Solve routing problems with a residual edge-graph attention neural network

1 code implementation6 May 2021 Kun Lei, Peng Guo, Yi Wang, Xiao Wu, Wenchao Zhao

In this paper, an end-to-end deep reinforcement learning framework is proposed to solve this type of combinatorial optimization problems.

Combinatorial Optimization Graph Attention +1

Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification

no code implementations4 May 2021 Yunhao Zhou, Yi Wang, Lap-Pui Chau

Specifically, all the feature embeddings of query and gallery images are expanded and enhanced by a linear combination of their neighbors, with the correlation prediction serves as discriminative combination weights.

Re-Ranking Vehicle Re-Identification

Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network

no code implementations4 May 2021 Chao Li, Hang Zhang, Jinwei Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed.

Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization

1 code implementation26 Apr 2021 Yi Wang, Xinyu Hou, Lap-Pui Chau

In this paper, we propose a simple yet effective crowd counting and localization network named SCALNet.

Crowd Counting

Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors

1 code implementation22 Apr 2021 Arman Maesumi, Mingkang Zhu, Yi Wang, Tianlong Chen, Zhangyang Wang, Chandrajit Bajaj

This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes.

Adversarial Attack

Machine-learned 3D Building Vectorization from Satellite Imagery

no code implementations13 Apr 2021 Yi Wang, Stefano Zorzi, Ksenia Bittner

We propose a machine learning based approach for automatic 3D building reconstruction and vectorization.

Semantic Segmentation

Learning Deep Latent Subspaces for Image Denoising

no code implementations1 Apr 2021 Yunhao Yang, Yuhan Zheng, Yi Wang, Chandrajit Bajaj

We compare our results to a conventional one-encoder-one-decoder architecture.

Image Denoising

Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction

no code implementations10 Mar 2021 Jinwei Zhang, Hang Zhang, Chao Li, Pascal Spincemaille, Mert Sabuncu, Thanh D. Nguyen, Yi Wang

Quantitative imaging in MRI usually involves acquisition and reconstruction of a series of images at multi-echo time points, which possibly requires more scan time and specific reconstruction technique compared to conventional qualitative imaging.

Image Reconstruction

Prevalent Behavior of Smooth Strongly Monotone Discrete-Time Dynamical Systems

no code implementations8 Mar 2021 Yi Wang, Jinxiang Yao, Yufeng Zhang

For C1-smooth strongly monotone discrete-time dynamical systems, it is shown that ``convergence to linearly stable cycles" is a prevalent asymptotic behavior in the measuretheoretic sense.

Dynamical Systems

NeRD: Neural Representation of Distribution for Medical Image Segmentation

no code implementations6 Mar 2021 Hang Zhang, Rongguang Wang, Jinwei Zhang, Chao Li, Gufeng Yang, Pascal Spincemaille, Thanh Nguyen, Yi Wang

We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an underlying function mapping image coordinates to the feature distribution.

Lesion Segmentation

A Comprehensive Review of Deep Learning-based Single Image Super-resolution

no code implementations18 Feb 2021 Syed Muhammad Arsalan Bashir, Yi Wang, Mahrukh Khan, Yilong Niu

This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution.

Image Super-Resolution

The Yamabe flow on asymptotically flat manifolds

no code implementations15 Feb 2021 Eric Chen, Yi Wang

We study the Yamabe flow starting from an asymptotically flat manifold $(M^n, g_0)$.

Differential Geometry Analysis of PDEs 53C18, 53Exx

Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher

no code implementations ICCV 2021 Yichen Zhu, Yi Wang

We formulate the knowledge distillation as a multi-task learning problem so that the teacher transfers knowledge to the student only if the student can benefit from learning such knowledge.

Image Classification Knowledge Distillation +3

SEGSys: A mapping system for segmentation analysis in energy

no code implementations11 Dec 2020 Xiufeng Liu, Rongling Li, Yi Wang, Per Sieverts Nielsen

This paper showcases the system on the segmentation analysis using an electricity consumption data set and validates the effectiveness of the system.


Enhance Convolutional Neural Networks with Noise Incentive Block

no code implementations9 Dec 2020 Menghan Xia, Yi Wang, Chu Han, Tien-Tsin Wong

Noise Incentive Block (NIB), which serves as a generic plug-in for any CNN generation model.

Image Generation Translation

RANet: Region Attention Network for Semantic Segmentation

1 code implementation NeurIPS 2020 Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin

In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image.

Semantic Segmentation

Rethinking and Designing a High-performing Automatic License Plate Recognition Approach

no code implementations30 Nov 2020 Yi Wang, Zhen-Peng Bian, Yunhao Zhou, Lap-Pui Chau

Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should abundant additional character segmentation and recurrent neural network (RNN), but adopt a plain convolutional neural network (CNN); (3) in the case of CNN, taking advantage of vertex information on license plates improves the recognition performance; and (4) the weight-sharing character classifier addresses the lack of training images in small-scale datasets.

Data Augmentation License Plate Detection +1

Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020

no code implementations20 Oct 2020 Shufan Shen, Ran Miao, Yi Wang, Zhihua Wei

In this report, we discribe the submission of Tongji University undergraduate team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020.

Data Augmentation Denoising +2

Ensembling Low Precision Models for Binary Biomedical Image Segmentation

no code implementations16 Oct 2020 Tianyu Ma, Hang Zhang, Hanley Ong, Amar Vora, Thanh D. Nguyen, Ajay Gupta, Yi Wang, Mert Sabuncu

Our core idea is straightforward: A diverse ensemble of low precision and high recall models are likely to make different false positive errors (classifying background as foreground in different parts of the image), but the true positives will tend to be consistent.

Lesion Segmentation Myocardium Segmentation +1

Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI

no code implementations12 Oct 2020 Tejas Sudharshan Mathai, Yi Wang, Nathan Cross

In this paper, we seek to quantify the bias in terms of the impact that different levels of motion artifacts have on the performance of neural networks engaged in a lesion segmentation task.

Curriculum Learning Lesion Segmentation

Geometric Loss for Deep Multiple Sclerosis lesion Segmentation

no code implementations29 Sep 2020 Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Susan A. Gauthier, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for training deep learning based segmentation models.

Lesion Segmentation

Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation

no code implementations13 Sep 2020 Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture global contextual information for performance enhancement.

Image Reconstruction Lesion Segmentation

Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mapping

no code implementations7 Sep 2020 Jinwei Zhang, Hang Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh Nguyen, Yi Wang

A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve the quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation.

Density Estimation

Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network

no code implementations31 Jul 2020 Junxiong Yu, Chaoyu Chen, Xin Yang, Yi Wang, Dan Yan, Jianxing Zhang, Dong Ni

The efficacy of our network is verified from a collected dataset of 418 patients with 145 benign tumors and 273 malignant tumors.

Breast Cancer Detection Classification +1

Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI

no code implementations28 Jul 2020 Jinwei Zhang, Hang Zhang, Alan Wang, Qihao Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang

The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the scanner, rather than simulated k-space data from magnitude MR images in LOUPE, was retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data; secondly, binary stochastic k-space sampling, rather than approximate stochastic k-space sampling of LOUPE during training, was applied together with a straight-through (ST) estimator to estimate the gradient of the threshold operation in a neural network; thirdly, modified unrolled optimization network, rather than modified U-Net in LOUPE, was used as the reconstruction network in order to reconstruct multi-coil data properly and reduce the dependency on training data.

A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

2 code implementations25 Jul 2020 Yi Wang, Junhui Hou, Xinyu Hou, Lap-Pui Chau

In this paper, we propose a novel self-training approach named Crowd-SDNet that enables a typical object detector trained only with point-level annotations (i. e., objects are labeled with points) to estimate both the center points and sizes of crowded objects.

Crowd Counting Object Detection

Cache-enabling UAV Communications: Network Deployment and Resource Allocation

no code implementations22 Jul 2020 Tiankui Zhang, Yi Wang, Yuanwei Liu, Wenjun Xu, Arumugam Nallanathan

We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS).

Can 3D Adversarial Logos Cloak Humans?

1 code implementation25 Jun 2020 Yi Wang, Jingyang Zhou, Tianlong Chen, Sijia Liu, Shiyu Chang, Chandrajit Bajaj, Zhangyang Wang

Contrary to the traditional adversarial patch, this new form of attack is mapped into the 3D object world and back-propagates to the 2D image domain through differentiable rendering.

Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes

1 code implementation28 Apr 2020 Xin Yang, Xu Wang, Yi Wang, Haoran Dou, Shengli Li, Huaxuan Wen, Yi Lin, Pheng-Ann Heng, Dong Ni

In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes.

When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks

no code implementations19 Apr 2020 Zhiyu Zhu, Zhen-Peng Bian, Junhui Hou, Yi Wang, Lap-Pui Chau

However, the existing networks usually suffer from either redundancy of convolutional layers or insufficient utilization of parameters.

Neural Architecture Search

Attentive Normalization for Conditional Image Generation

1 code implementation CVPR 2020 Yi Wang, Ying-Cong Chen, Xiangyu Zhang, Jian Sun, Jiaya Jia

Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain.

Conditional Image Generation Semantic correspondence +2

VCNet: A Robust Approach to Blind Image Inpainting

2 code implementations ECCV 2020 Yi Wang, Ying-Cong Chen, Xin Tao, Jiaya Jia

Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image.

Image Inpainting

PointINS: Point-based Instance Segmentation

no code implementations13 Mar 2020 Lu Qi, Yi Wang, Yukang Chen, Yingcong Chen, Xiangyu Zhang, Jian Sun, Jiaya Jia

In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features.

Instance Segmentation Object Detection +2

Convolutional Neural Networks with Dynamic Regularization

no code implementations26 Sep 2019 Yi Wang, Zhen-Peng Bian, Junhui Hou, Lap-Pui Chau

That is, the regularization strength is fixed to a predefined schedule, and manual adjustments are required to adapt to various network architectures.

Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

no code implementations31 Jul 2019 Yi Wang, Shiqi Zhang, Joohyung Lee

In this paper, we present a unified framework to integrate icorpp's reasoning and planning components.

Decision Making

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

1 code implementation3 Jul 2019 Yi Wang, Haoran Dou, Xiao-Wei Hu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng-Ann Heng, Tianfu Wang, Dong Ni

Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers.

Medical Image Segmentation

Fully Decoupled Neural Network Learning Using Delayed Gradients

1 code implementation21 Jun 2019 Huiping Zhuang, Yi Wang, Qinglai Liu, Shuai Zhang, Zhiping Lin

Training neural networks with back-propagation (BP) requires a sequential passing of activations and gradients, which forces the network modules to work in a synchronous fashion.

Wide-Context Semantic Image Extrapolation

2 code implementations CVPR 2019 Yi Wang, Xin Tao, Xiaoyong Shen, Jiaya Jia

This paper studies the fundamental problem of extrapolating visual context using deep generative models, i. e., extending image borders with plausible structure and details.

Image Outpainting

Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+

no code implementations1 Apr 2019 Yi Wang, Joohyung Lee

Alternatively, the semantics of pBC+ can also be defined in terms of Markov Decision Process (MDP), which in turn allows for representing MDP in a succinct and elaboration tolerant way as well as to leverage an MDP solver to compute pBC+.

Understanding and Comparing Scalable Gaussian Process Regression for Big Data

no code implementations3 Nov 2018 Haitao Liu, Jianfei Cai, Yew-Soon Ong, Yi Wang

This paper devotes to investigating the methodological characteristics and performance of representative global and local scalable GPs including sparse approximations and local aggregations from four main perspectives: scalability, capability, controllability and robustness.

Weight Learning in a Probabilistic Extension of Answer Set Programs

no code implementations14 Aug 2018 Joohyung Lee, Yi Wang

Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains.

The conditional permutation test for independence while controlling for confounders

no code implementations14 Jul 2018 Thomas B. Berrett, Yi Wang, Rina Foygel Barber, Richard J. Samworth

Like the conditional randomization test of Cand\`es et al. (2018), our test relies on the availability of an approximation to the distribution of $X \mid Z$.

Methodology Statistics Theory Statistics Theory

Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

1 code implementation ICML 2018 Haitao Liu, Jianfei Cai, Yi Wang, Yew-Soon Ong

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts.

Distributed Computing

A Probabilistic Extension of Action Language BC+

no code implementations2 May 2018 Joohyung Lee, Yi Wang

We present a probabilistic extension of action language BC+.

Scale-recurrent Network for Deep Image Deblurring

4 code implementations CVPR 2018 Xin Tao, Hongyun Gao, Yi Wang, Xiaoyong Shen, Jue Wang, Jiaya Jia

In single image deblurring, the "coarse-to-fine" scheme, i. e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches.

Deblurring Image Relighting

Online Robust Image Alignment via Subspace Learning From Gradient Orientations

no code implementations ICCV 2017 Qingqing Zheng, Yi Wang, Pheng-Ann Heng

The proposed method integrates the subspace learning, transformed IGO reconstruction and image alignment into a unified online framework, which is robust for aligning images with severe intensity distortions.

Face Recognition

Computing LPMLN Using ASP and MLN Solvers

no code implementations19 Jul 2017 Joohyung Lee, Samidh Talsania, Yi Wang

LPMLN is a recent addition to probabilistic logic programming languages.

Incremental Kernel Null Space Discriminant Analysis for Novelty Detection

no code implementations CVPR 2017 Juncheng Liu, Zhouhui Lian, Yi Wang, Jianguo Xiao

This validates the superiority of our IKNDA against the state of the art in novelty detection for large-scale data.

Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images

no code implementations6 Dec 2016 Xin Yang, Lequan Yu, Lingyun Wu, Yi Wang, Dong Ni, Jing Qin, Pheng-Ann Heng

Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.

Medical Image Segmentation

On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic

no code implementations28 Jun 2016 Joohyung Lee, Yi Wang

Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL) are widely applied formalisms in Statistical Relational Learning, an emerging area in Artificial Intelligence that is concerned with combining logical and statistical AI.

Relational Reasoning

Stochastic Patching Process

no code implementations23 May 2016 Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen

Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions.

Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problem

no code implementations22 May 2016 Peng Guo, Wenming Cheng, Yi Wang

This paper considers the two-stage capacitated facility location problem (TSCFLP) in which products manufactured in plants are delivered to customers via storage depots.

Consistency Analysis of Nearest Subspace Classifier

no code implementations24 Jan 2015 Yi Wang

The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace.

General Classification

Random Bits Regression: a Strong General Predictor for Big Data

no code implementations13 Jan 2015 Yi Wang, Yi Li, Momiao Xiong, Li Jin

To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR).

Maximum mutual information regularized classification

no code implementations27 Sep 2014 Jim Jing-Yan Wang, Yi Wang, Shiguang Zhao, Xin Gao

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label.

Classification General Classification

Tensity Research Based on the Information of Eye Movement

no code implementations17 Sep 2014 Yi Wang

User's mental state is concerned gradually, during the interaction course of human robot.

Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning

no code implementations CVPR 2014 Bang Zhang, Yi Wang, Yang Wang, Fang Chen

Many prevalent multi-class classification approaches can be unified and generalized by the output coding framework which usually consists of three phases: (1) coding, (2) learning binary classifiers, and (3) decoding.

General Classification Multi-class Classification +1

Latent Tree Models and Approximate Inference in Bayesian Networks

no code implementations15 Jan 2014 Yi Wang, Nevin L. Zhang, Tao Chen

We propose a novel method for approximate inference in Bayesian networks (BNs).

Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach

no code implementations6 Nov 2013 Xinxi Wang, Yi Wang, David Hsu, Ye Wang

Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings.

Bayesian Inference Recommendation Systems +1

Parallel machine scheduling with step deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm

no code implementations2 Sep 2013 Peng Guo, Wenming Cheng, Yi Wang

Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem.

MathGR: a tensor and GR computation package to keep it simple

1 code implementation6 Jun 2013 Yi Wang

We introduce the MathGR package, written in Mathematica.

Mathematical Software Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Theory Computational Physics

Monte Carlo Bayesian Reinforcement Learning

no code implementations27 Jun 2012 Yi Wang, Kok Sung Won, David Hsu, Wee Sun Lee

Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them.

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