Wayne State University

∙ Detroit , United States ∙ 0 followers

A premier research university serving a diverse body of motivated students in vibrant Midtown, the cultural center of Detroit.

On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Deep convolutional neural networks (CNNs) trained with logistic and soft...
03/04/2020 ∙ by Xiangrui Li, et al. ∙ 37 ∙ share

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SPA-GAN: Spatial Attention GAN for Image-to-Image Translation

Image-to-image translation is to learn a mapping between images from a s...
08/19/2019 ∙ by Hajar Emami, et al. ∙ 25 ∙ share

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COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray

With the increasing demand for millions of COVID-19 screenings, Computed...
04/06/2020 ∙ by Xin Li, et al. ∙ 24 ∙ share

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Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability

Latent factor collaborative filtering (CF) has been a widely used techni...
07/12/2020 ∙ by Deng Pan, et al. ∙ 21 ∙ share

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Defending against adversarial attacks on medical imaging AI system, classification or detection?

Medical imaging AI systems such as disease classification and segmentati...
06/24/2020 ∙ by Xin Li, et al. ∙ 18 ∙ share

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Improve SGD Training via Aligning Min-batches

Deep neural networks (DNNs) for supervised learning can be viewed as a p...
02/23/2020 ∙ by Xiangrui Li, et al. ∙ 15 ∙ share

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COVID-Xpert: An AI Powered Population Screening of COVID-19 Cases Using Chest Radiography Images

With the increasing demand for millions of COVID-19 tests, Computed Tomo...
04/06/2020 ∙ by Xin Li, et al. ∙ 15 ∙ share

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Improve SGD Training via Aligning Mini-batches

Deep neural networks (DNNs) for supervised learning can be viewed as a p...
02/23/2020 ∙ by Xiangrui Li, et al. ∙ 7 ∙ share

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SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Traditional medicine typically applies one-size-fits-all treatment for t...
09/26/2017 ∙ by Milad Zafar Nezhad, et al. ∙ 1 ∙ share

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SAFS: A Deep Feature Selection Approach for Precision Medicine

In this paper, we propose a new deep feature selection method based on d...
04/20/2017 ∙ by Milad Zafar Nezhad, et al. ∙ 1 ∙ share

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A Predictive Approach Using Deep Feature Learning for Electronic Medical Records: A Comparative Study

Massive amount of electronic medical records accumulating from patients ...
01/06/2018 ∙ by Milad Zafar Nezhad, et al. ∙ 1 ∙ share

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Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks

Understanding human fetal neurodevelopment is of great clinical importan...
06/09/2019 ∙ by Xiangrui Li, et al. ∙ 1 ∙ share

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Vispi: Automatic Visual Perception and Interpretation of Chest X-rays

Medical imaging contains the essential information for rendering diagnos...
06/12/2019 ∙ by Xin Li, et al. ∙ 1 ∙ share

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Tackling Multiple Ordinal Regression Problems: Sparse and Deep Multi-Task Learning Approaches

Many real-world datasets are labeled with natural orders, i.e., ordinal ...
07/29/2019 ∙ by Lu Wang, et al. ∙ 1 ∙ share

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Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach

Existing aspect based sentiment analysis (ABSA) approaches leverage vari...
03/22/2020 ∙ by Yao Qiang, et al. ∙ 1 ∙ share

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A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip

In building intelligent transportation systems such as taxi or rideshare...
10/12/2017 ∙ by Ishan Jindal, et al. ∙ 0 ∙ share

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Learning Deep Networks from Noisy Labels with Dropout Regularization

Large datasets often have unreliable labels-such as those obtained from ...
05/09/2017 ∙ by Ishan Jindal, et al. ∙ 0 ∙ share

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Performance Limits on the Classification of Kronecker-structured Models

Kronecker-structured (K-S) models recently have been proposed for the ef...
05/07/2017 ∙ by Ishan Jindal, et al. ∙ 0 ∙ share

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Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks

Motivated by machine learning applications in networks of sensors, inter...
04/25/2017 ∙ by Matthew Nokleby, et al. ∙ 0 ∙ share

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Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild

Face detection methods have relied on face datasets for training. Howeve...
06/27/2017 ∙ by Tarik Alafif, et al. ∙ 0 ∙ share

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On the performance of a hybrid genetic algorithm in dynamic environments

The ability to track the optimum of dynamic environments is important in...
02/22/2013 ∙ by Quan Yuan, et al. ∙ 0 ∙ share

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A Weight-coded Evolutionary Algorithm for the Multidimensional Knapsack Problem

A revised weight-coded evolutionary algorithm (RWCEA) is proposed for so...
02/21/2013 ∙ by Quan Yuan, et al. ∙ 0 ∙ share

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Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery

Evaluating surgeon skill has predominantly been a subjective task. Devel...
11/16/2016 ∙ by Mahtab J. Fard, et al. ∙ 0 ∙ share

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Rate-Distortion Bounds on Bayes Risk in Supervised Learning

We present an information-theoretic framework for bounding the number of...
05/08/2016 ∙ by Matthew Nokleby, et al. ∙ 0 ∙ share

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Comments on "On Approximating Euclidean Metrics by Weighted t-Cost Distances in Arbitrary Dimension"

Mukherjee (Pattern Recognition Letters, vol. 32, pp. 824-831, 2011) rece...
06/10/2012 ∙ by M. Emre Celebi, et al. ∙ 0 ∙ share

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Fast Color Space Transformations Using Minimax Approximations

Color space transformations are frequently used in image processing, gra...
09/04/2010 ∙ by M. Emre Celebi, et al. ∙ 0 ∙ share

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On Euclidean Norm Approximations

Euclidean norm calculations arise frequently in scientific and engineeri...
08/28/2010 ∙ by M. Emre Celebi, et al. ∙ 0 ∙ share

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Multi-scale Spectrum Sensing in 5G Cognitive Networks

A multi-scale approach to spectrum sensing is proposed to overcome the h...
02/23/2018 ∙ by Nicolo Michelusi, et al. ∙ 0 ∙ share

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A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer

Survival analysis has been developed and applied in the number of areas ...
04/10/2018 ∙ by Milad Zafar Nezhad, et al. ∙ 0 ∙ share

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Data-Driven Analytics for Benchmarking and Optimizing Retail Store Performance

Growing competitiveness and increasing availability of data is generatin...
06/14/2018 ∙ by Haidar Almohri, et al. ∙ 0 ∙ share

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Unified Scheduling for Predictable Communication Reliability in Cellular Networks with D2D Links

Cellular networks with D2D links are increasingly being explored for mis...
07/02/2018 ∙ by Yuwei Xie, et al. ∙ 0 ∙ share

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Co-KV: A Collaborative Key-Value Store Using Near-Data Processing to Improve Compaction for the LSM-tree

Log-structured merge tree (LSM-tree) based key-value stores are widely e...
07/11/2018 ∙ by Hui Sun, et al. ∙ 0 ∙ share

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CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles

Connected and autonomous vehicles (CAVs) have recently attracted a signi...
10/15/2018 ∙ by Yifan Wang, et al. ∙ 0 ∙ share

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Tensor Matched Kronecker-Structured Subspace Detection for Missing Information

We consider the problem of detecting whether a tensor signal having many...
10/25/2018 ∙ by Ishan Jindal, et al. ∙ 0 ∙ share

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Information Bottleneck Methods for Distributed Learning

We study a distributed learning problem in which Alice sends a compresse...
10/26/2018 ∙ by Parinaz Farajiparvar, et al. ∙ 0 ∙ share

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Understanding the Motivations, Challenges and Needs of Blockchain Software Developers: A Survey

The blockchain technology has potential applications in various areas su...
11/10/2018 ∙ by Amiangshu Bosu, et al. ∙ 0 ∙ share

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Expressions of Sentiments During Code Reviews: Male vs. Female

Background: As most of the software development organizations are male-d...
12/13/2018 ∙ by Rajshakhar Paul, et al. ∙ 0 ∙ share

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A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization

Edge caching is being explored as a promising technology to alleviate th...
02/19/2019 ∙ by Yao Zhang, et al. ∙ 0 ∙ share

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An Effective Label Noise Model for DNN Text Classification

Because large, human-annotated datasets suffer from labeling errors, it ...
03/18/2019 ∙ by Ishan Jindal, et al. ∙ 0 ∙ share

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An Asynchronous, Decentralized Solution Framework for the Large Scale Unit Commitment Problem

With increased reliance on cyber infrastructure, large scale power netwo...
04/07/2019 ∙ by Paritosh Ramanan, et al. ∙ 0 ∙ share

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A-CNN: Annularly Convolutional Neural Networks on Point Clouds

Analyzing the geometric and semantic properties of 3D point clouds throu...
04/16/2019 ∙ by Artem Komarichev, et al. ∙ 0 ∙ share

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Attention-based Deep Reinforcement Learning for Multi-view Environments

In reinforcement learning algorithms, it is a common practice to account...
05/10/2019 ∙ by Elaheh Barati, et al. ∙ 0 ∙ share

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OpenEI: An Open Framework for Edge Intelligence

In the last five years, edge computing has attracted tremendous attentio...
06/05/2019 ∙ by Xingzhou Zhang, et al. ∙ 0 ∙ share

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CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification

Classification on imbalanced datasets is a challenging task in real-worl...
06/10/2019 ∙ by Xiangrui Li, et al. ∙ 0 ∙ share

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An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments

In reinforcement learning algorithms, leveraging multiple views of the e...
07/19/2019 ∙ by Elaheh Barati, et al. ∙ 0 ∙ share

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Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study

Increasing volume of Electronic Health Records (EHR) in recent years pro...
08/24/2019 ∙ by Najibesadat Sadati, et al. ∙ 0 ∙ share

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On Multi-Cascade Influence Maximization: Model, Hardness and Algorithmic Framework

This paper studies the multi-cascade influence maximization problem, whi...
11/30/2019 ∙ by Guangmo Tong, et al. ∙ 0 ∙ share

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Unconditionally energy stable discontinuous Galerkin schemes for the Cahn-Hilliard equation

In this paper, we introduce novel discontinuous Galerkin (DG) schemes fo...
12/21/2019 ∙ by Hailiang Liu, et al. ∙ 0 ∙ share

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Analysis of adaptive BDF2 scheme for diffusion equations

The variable two-step backward differentiation formula (BDF2) is revisit...
12/24/2019 ∙ by Hong-lin Liao, et al. ∙ 0 ∙ share

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On the loss of learning capability inside an arrangement of neural networks

We analyze the loss of information and the loss of learning capability i...
01/09/2020 ∙ by Ivan Arraut, et al. ∙ 0 ∙ share

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