Paper Group ANR 904
Affordable Uplift: Supervised Randomization in Controlled Experiments. Using AI for Economic Upliftment of Handicraft Industry. Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures. Bayesian Relational Memory for Semantic Visual Navigation. LAC-Nav: Collision-Free Mutiagent Navigation Based on The Local Action Cells. N …
Affordable Uplift: Supervised Randomization in Controlled Experiments
| Title | Affordable Uplift: Supervised Randomization in Controlled Experiments |
| Authors | Johannes Haupt, Daniel Jacob, Robin M. Gubela, Stefan Lessmann |
| Abstract | Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment assignment is costly, since random targeting deviates from an established targeting policy. To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization. It is a novel approach that integrates existing scoring models into randomized trials to target relevant customers, while ensuring consistent estimates of treatment effects through correction for active sample selection. An empirical Monte Carlo study shows that data collection under supervised randomization is cost-efficient, while downstream uplift models perform competitively. |
| Tasks | Decision Making |
| Published | 2019-10-01 |
| URL | https://arxiv.org/abs/1910.00393v1 |
| https://arxiv.org/pdf/1910.00393v1.pdf | |
| PWC | https://paperswithcode.com/paper/affordable-uplift-supervised-randomization-in |
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Using AI for Economic Upliftment of Handicraft Industry
| Title | Using AI for Economic Upliftment of Handicraft Industry |
| Authors | Nitya Raviprakash, Sonam Damani, Ankush Chatterjee, Meghana Joshi, Puneet Agrawal |
| Abstract | The handicraft industry is a strong pillar of Indian economy which provides large-scale employment opportunities to artisans in rural and underprivileged communities. However, in this era of globalization, diverse modern designs have rendered traditional designs old and monotonous, causing an alarming decline of handicraft sales. For this age-old industry to survive the global competition, it is imperative to integrate contemporary designs with Indian handicrafts. In this paper, we use novel AI techniques to generate contemporary designs for two popular Indian handicrafts - Ikat and Block Print. These techniques were successfully employed by communities across India to manufacture and sell products with greater appeal and revenue. The designs are evaluated to be significantly more likeable and marketable than the current designs used by artisans. |
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| Published | 2019-05-31 |
| URL | https://arxiv.org/abs/1907.02014v1 |
| https://arxiv.org/pdf/1907.02014v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-ai-for-economic-upliftment-of |
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Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures
| Title | Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures |
| Authors | Andre Viebke, Sabri Pllana, Suejb Memeti, Joanna Kolodziej |
| Abstract | Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requires parallel computing resources. In this paper, we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture. While for the first performance model we minimally use measurement techniques for parameter value estimation, in the second model we estimate more parameters based on measurements. We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi. The achieved average performance prediction accuracy is about 15% for the first model and 11% for second model. |
| Tasks | Object Detection |
| Published | 2019-06-04 |
| URL | https://arxiv.org/abs/1906.01992v1 |
| https://arxiv.org/pdf/1906.01992v1.pdf | |
| PWC | https://paperswithcode.com/paper/performance-modelling-of-deep-learning-on |
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Bayesian Relational Memory for Semantic Visual Navigation
| Title | Bayesian Relational Memory for Semantic Visual Navigation |
| Authors | Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian |
| Abstract | We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards. BRM takes the form of a probabilistic relation graph over semantic entities (e.g., room types), which allows (1) capturing the layout prior from training environments, i.e., prior knowledge, (2) estimating posterior layout at test time, i.e., memory update, and (3) efficient planning for navigation, altogether. We develop a BRM agent consisting of a BRM module for producing sub-goals and a goal-conditioned locomotion module for control. When testing in unseen environments, the BRM agent outperforms baselines that do not explicitly utilize the probabilistic relational memory structure |
| Tasks | Visual Navigation |
| Published | 2019-09-10 |
| URL | https://arxiv.org/abs/1909.04306v1 |
| https://arxiv.org/pdf/1909.04306v1.pdf | |
| PWC | https://paperswithcode.com/paper/bayesian-relational-memory-for-semantic |
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LAC-Nav: Collision-Free Mutiagent Navigation Based on The Local Action Cells
| Title | LAC-Nav: Collision-Free Mutiagent Navigation Based on The Local Action Cells |
| Authors | Li Ning, Yong Zhang |
| Abstract | Collision avoidance is one of the most primary requirement in the decentralized multiagent navigations: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduce the concept of local action cell, which provides for each agent a set of velocities that are safe to perform. Based on the realtime updated local action cells, we propose the LAC-Nav approach to navigate the agent with the properly selected velocity; and furthermore, we coupled the local action cell with an adaptive learning framework, in which the effect of selections are evaluated and used as the references for making decisions in the following updates. Through the experiments for three commonly considered scenarios, we demonstrated the efficiency of the proposed approaches, with the comparison to several widely studied strategies. |
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| Published | 2019-11-12 |
| URL | https://arxiv.org/abs/1911.04646v1 |
| https://arxiv.org/pdf/1911.04646v1.pdf | |
| PWC | https://paperswithcode.com/paper/lac-nav-collision-free-mutiagent-navigation |
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Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
| Title | Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking |
| Authors | Wentao Zhu, Yufang Huang, Mani A Vannan, Shizhen Liu, Daguang Xu, Wei Fan, Zhen Qian, Xiaohui Xie |
| Abstract | Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocardiogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking. NMSR incorporates two novel components: 1) utilizing a deep neural net to parameterize the velocity field between two image frames, and 2) optimizing the parameters of the neural net in a sequential multi-scale fashion to account for large variations within the velocity field. Experiments demonstrate that NMSR yields significantly better registration accuracy than state-of-the-art methods, such as advanced normalization tools (ANTs) and VoxelMorph, for both myocardial and cardiac blood flow dense tracking. Our approach promises to provide a fully automated method for fast and accurate analyses of echocardiograms. |
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| Published | 2019-06-18 |
| URL | https://arxiv.org/abs/1906.07357v1 |
| https://arxiv.org/pdf/1906.07357v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-multi-scale-self-supervised |
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Zoom in to where it matters: a hierarchical graph based model for mammogram analysis
| Title | Zoom in to where it matters: a hierarchical graph based model for mammogram analysis |
| Authors | Hao Du, Jiashi Feng, Mengling Feng |
| Abstract | In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination. Inspired by this observation, we propose a hierarchical graph neural network to detect abnormal lesions from medical images by automatically zooming into ROIs. We focus on mammogram analysis for breast cancer diagnosis for this study. Our proposed network consist of two graph attention networks performing two tasks: (1) node classification to predict whether to zoom into next level; (2) graph classification to classify whether a mammogram is normal/benign or malignant. The model is trained and evaluated on INbreast dataset and we obtain comparable AUC with state-of-the-art methods. |
| Tasks | Graph Classification, Node Classification |
| Published | 2019-12-16 |
| URL | https://arxiv.org/abs/1912.07517v1 |
| https://arxiv.org/pdf/1912.07517v1.pdf | |
| PWC | https://paperswithcode.com/paper/zoom-in-to-where-it-matters-a-hierarchical |
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Progressive Generative Adversarial Networks for Medical Image Super resolution
| Title | Progressive Generative Adversarial Networks for Medical Image Super resolution |
| Authors | Dwarikanath Mahapatra, Behzad Bozorgtabar |
| Abstract | Anatomical landmark segmentation and pathology localization are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GAN) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor. The super resolved images can be used for more accurate detection of landmarks and pathology. Our primary contribution is in proposing a multistage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function. The triplet loss enables stepwise image quality improvement by using the output of the previous stage as the baseline. This facilitates generation of super resolved images of high scaling factor while maintaining good image quality. Experimental results for image super-resolution show that our proposed multistage P-GAN outperforms competing methods and baseline GAN. |
| Tasks | Image Super-Resolution, Super-Resolution |
| Published | 2019-02-06 |
| URL | http://arxiv.org/abs/1902.02144v2 |
| http://arxiv.org/pdf/1902.02144v2.pdf | |
| PWC | https://paperswithcode.com/paper/progressive-generative-adversarial-networks |
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Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
| Title | Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping |
| Authors | Mengyuan Yan, Adrian Li, Mrinal Kalakrishnan, Peter Pastor |
| Abstract | Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time to infer the best action from the value network. As a result, the inference time grows exponentially as the dimension of action space increases. We propose an alternative method, by directly training a neural density model to approximate the conditional distribution of successful grasp poses from the input images. We construct a neural network that combines Gaussian mixture and normalizing flows, which is able to represent multi-modal, complex probability distributions. We demonstrate on both simulation and real robot that the proposed actor model achieves similar performance compared to the value network using the Cross-Entropy Method (CEM) for inference, on top-down grasping with a 4 dimensional action space. Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method. We believe that actor models will play an important role when scaling up these approaches to higher dimensional action spaces. |
| Tasks | Robotic Grasping |
| Published | 2019-04-15 |
| URL | http://arxiv.org/abs/1904.07319v1 |
| http://arxiv.org/pdf/1904.07319v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-probabilistic-multi-modal-actor |
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Lung cancer screening with low-dose CT scans using a deep learning approach
| Title | Lung cancer screening with low-dose CT scans using a deep learning approach |
| Authors | Jason L. Causey, Yuanfang Guan, Wei Dong, Karl Walker, Jake A. Qualls, Fred Prior, Xiuzhen Huang |
| Abstract | Lung cancer is the leading cause of cancer deaths. Early detection through low-dose computed tomography (CT) screening has been shown to significantly reduce mortality but suffers from a high false positive rate that leads to unnecessary diagnostic procedures. Quantitative image analysis coupled to deep learning techniques has the potential to reduce this false positive rate. We conducted a computational analysis of 1449 low-dose CT studies drawn from the National Lung Screening Trial (NLST) cohort. We applied to this cohort our newly developed algorithm, DeepScreener, which is based on a novel deep learning approach. The algorithm, after the training process using about 3000 CT studies, does not require lung nodule annotations to conduct cancer prediction. The algorithm uses consecutive slices and multi-task features to determine whether a nodule is likely to be cancer, and a spatial pyramid to detect nodules at different scales. We find that the algorithm can predict a patient’s cancer status from a volumetric lung CT image with high accuracy (78.2%, with area under the Receiver Operating Characteristic curve (AUC) of 0.858). Our preliminary framework ranked 16th of 1972 teams (top 1%) in the Data Science Bowl 2017 (DSB2017) competition, based on the challenge datasets. We report here the application of DeepScreener on an independent NLST test set. This study indicates that the deep learning approach has the potential to significantly reduce the false positive rate in lung cancer screening with low-dose CT scans. |
| Tasks | Computed Tomography (CT) |
| Published | 2019-06-01 |
| URL | https://arxiv.org/abs/1906.00240v1 |
| https://arxiv.org/pdf/1906.00240v1.pdf | |
| PWC | https://paperswithcode.com/paper/190600240 |
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Dual-branch residual network for lung nodule segmentation
| Title | Dual-branch residual network for lung nodule segmentation |
| Authors | Haichao Cao, Hong Liu, Enmin Song, Chih-Cheng Hung, Guangzhi Ma, Xiangyang Xu, Renchao Jin, Jianguo Lu |
| Abstract | An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: 1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; 2) we combine the features of the intensity and the convolution neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with an average dice score of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our average dice score was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist. |
| Tasks | Computed Tomography (CT), Lung Nodule Segmentation |
| Published | 2019-05-21 |
| URL | https://arxiv.org/abs/1905.08413v1 |
| https://arxiv.org/pdf/1905.08413v1.pdf | |
| PWC | https://paperswithcode.com/paper/dual-branch-residual-network-for-lung-nodule |
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Non-Autoregressive Machine Translation with Auxiliary Regularization
| Title | Non-Autoregressive Machine Translation with Auxiliary Regularization |
| Authors | Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, Tie-Yan Liu |
| Abstract | As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference. |
| Tasks | Machine Translation |
| Published | 2019-02-22 |
| URL | http://arxiv.org/abs/1902.10245v1 |
| http://arxiv.org/pdf/1902.10245v1.pdf | |
| PWC | https://paperswithcode.com/paper/non-autoregressive-machine-translation-with |
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Mining Association Rules in Various Computing Environments: A Survey
| Title | Mining Association Rules in Various Computing Environments: A Survey |
| Authors | Sudhakar Singh, Pankaj Singh, Rakhi Garg, P. K. Mishra |
| Abstract | Association Rule Mining (ARM) is one of the well know and most researched technique of data mining. There are so many ARM algorithms have been designed that their counting is a large number. In this paper we have surveyed the various ARM algorithms in four computing environments. The considered computing environments are sequential computing, parallel and distributed computing, grid computing and cloud computing. With the emergence of new computing paradigm, ARM algorithms have been designed by many researchers to improve the efficiency by utilizing the new paradigm. This paper represents the journey of ARM algorithms started from sequential algorithms, and through parallel and distributed, and grid based algorithms to the current state-of-the-art, along with the motives for adopting new machinery. |
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| Published | 2019-06-30 |
| URL | https://arxiv.org/abs/1908.07918v1 |
| https://arxiv.org/pdf/1908.07918v1.pdf | |
| PWC | https://paperswithcode.com/paper/mining-association-rules-in-various-computing |
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A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT
| Title | A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT |
| Authors | Sulaiman Vesal, Nishant Ravikumar, Andreas Maier |
| Abstract | Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereby facilitating adaptive and computer-assisted radiotherapy. Although manual delineation of OARs is still highly prevalent, it is prone to errors due to complex variations in the shape and position of organs across patients, and low soft tissue contrast between neighbouring organs in CT images. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. In this paper, we propose a deep learning framework to segment OARs in thoracic CT images, specifically for the: heart, esophagus, trachea and aorta. Our approach employs dilated convolutions and aggregated residual connections in the bottleneck of a U-Net styled network, which incorporates global context and dense information. Our method achieved an overall Dice score of 91.57% on 20 unseen test samples from the ISBI 2019 SegTHOR challenge. |
| Tasks | Computed Tomography (CT), Medical Image Segmentation, Semantic Segmentation |
| Published | 2019-05-19 |
| URL | https://arxiv.org/abs/1905.07710v1 |
| https://arxiv.org/pdf/1905.07710v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-2d-dilated-residual-u-net-for-multi-organ |
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X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
| Title | X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks |
| Authors | Xingde Ying, Heng Guo, Kai Ma, Jian Wu, Zhengxin Weng, Yefeng Zheng |
| Abstract | Computed tomography (CT) can provide a 3D view of the patient’s internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too. Traditional CT reconstruction methods require hundreds of X-ray projections through a full rotational scan of the body, which cannot be performed on a typical X-ray machine. In this work, we propose to reconstruct CT from two orthogonal X-rays using the generative adversarial network (GAN) framework. A specially designed generator network is exploited to increase data dimension from 2D (X-rays) to 3D (CT), which is not addressed in previous research of GAN. A novel feature fusion method is proposed to combine information from two X-rays.The mean squared error (MSE) loss and adversarial loss are combined to train the generator, resulting in a high-quality CT volume both visually and quantitatively. Extensive experiments on a publicly available chest CT dataset demonstrate the effectiveness of the proposed method. It could be a nice enhancement of a low-cost X-ray machine to provide physicians a CT-like 3D volume in several niche applications. |
| Tasks | Computed Tomography (CT) |
| Published | 2019-05-16 |
| URL | https://arxiv.org/abs/1905.06902v1 |
| https://arxiv.org/pdf/1905.06902v1.pdf | |
| PWC | https://paperswithcode.com/paper/x2ct-gan-reconstructing-ct-from-biplanar-x |
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