Paper Group ANR 1263
A Fuzzy Inference System for the Identification. Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation. Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks. Machine-assisted annotation of forensic imagery. Fast communication-efficient spectral clustering over dis …
A Fuzzy Inference System for the Identification
| Title | A Fuzzy Inference System for the Identification |
| Authors | Jose de Jesus Rubio, Ramon Silva Ortigoza, Francisco Jacob Avila, Adolfo Melendez, Juan Manuel Stein |
| Abstract | Odor identification is an important area in a wide range of industries like cosmetics, food, beverages and medical diagnosis among others. Odor detection could be done through an array of gas sensors conformed as an electronic nose where a data acquisition module converts sensor signals to a standard output to be analyzed. To facilitate odors detection a system is required for the identification. This paper presents the results of an automated odor identification process implemented by a fuzzy system and an electronic nose. First, an electronic nose prototype is manufactured to detect organic compounds vapor using an array of five tin dioxide gas sensors, an arduino uno board is used as a data acquisition section. Second, an intelligent module with a fuzzy system is considered for the identification of the signals received by the electronic nose. This solution proposes a system to identify odors by using a personal computer. Results show an acceptable precision. |
| Tasks | Medical Diagnosis |
| Published | 2019-05-02 |
| URL | https://arxiv.org/abs/1905.00991v1 |
| https://arxiv.org/pdf/1905.00991v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-fuzzy-inference-system-for-the |
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Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation
| Title | Bayesian Non-Parametric Multi-Source Modelling Based Determined Blind Source Separation |
| Authors | Chaitanya Narisetty, Tatsuya Komatsu, Reishi Kondo |
| Abstract | This paper proposes a determined blind source separation method using Bayesian non-parametric modelling of sources. Conventionally source signals are separated from a given set of mixture signals by modelling them using non-negative matrix factorization (NMF). However in NMF, a latent variable signifying model complexity must be appropriately specified to avoid over-fitting or under-fitting. As real-world sources can be of varying and unknown complexities, we propose a Bayesian non-parametric framework which is invariant to such latent variables. We show that our proposed method adapts to different source complexities, while conventional methods require parameter tuning for optimal separation. |
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| Published | 2019-04-08 |
| URL | http://arxiv.org/abs/1904.03787v1 |
| http://arxiv.org/pdf/1904.03787v1.pdf | |
| PWC | https://paperswithcode.com/paper/bayesian-non-parametric-multi-source |
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Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
| Title | Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks |
| Authors | Felipe Moser, Ruobing Huang, Aris T. Papageorghiou, Bartlomiej W. Papiez, Ana I. L. Namburete |
| Abstract | To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives. |
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| Published | 2019-11-18 |
| URL | https://arxiv.org/abs/1911.07566v2 |
| https://arxiv.org/pdf/1911.07566v2.pdf | |
| PWC | https://paperswithcode.com/paper/automated-fetal-brain-extraction-from |
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Machine-assisted annotation of forensic imagery
| Title | Machine-assisted annotation of forensic imagery |
| Authors | Sara Mousavi, Ramin Nabati, Megan Kleeschulte, Audris Mockus |
| Abstract | Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasible way to annotate very large collections, but leading approaches rely on large samples of completely and accurately annotated images. In the case of a large forensic collection, we are aiming to annotate, neither the complete annotation nor the large training samples can be feasibly produced. We, therefore, investigate ways to assist manual annotation efforts done by forensic experts. We present a method that can propose both images and areas within an image likely to contain desired classes. Evaluation of the method with human annotators showed highly accurate classification that was strongly helped by transfer learning. The segmentation precision (mAP) was improved by adding a separate class capturing background, but that did not affect the recall (mAR). Further work is needed to both increase the accuracy of segmentation and enhances prediction with additional covariates affecting decomposition. We hope this effort to be of help in other domains that require weak segmentation and have limited availability of qualified annotators. |
| Tasks | Transfer Learning |
| Published | 2019-02-28 |
| URL | http://arxiv.org/abs/1902.10848v1 |
| http://arxiv.org/pdf/1902.10848v1.pdf | |
| PWC | https://paperswithcode.com/paper/machine-assisted-annotation-of-forensic |
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Fast communication-efficient spectral clustering over distributed data
| Title | Fast communication-efficient spectral clustering over distributed data |
| Authors | Donghui Yan, Yingjie Wang, Jin Wang, Guodong Wu, Honggang Wang |
| Abstract | The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume {\it all the data are already in one place}, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with “minimal” communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing; the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while about 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing. |
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| Published | 2019-05-05 |
| URL | https://arxiv.org/abs/1905.01596v1 |
| https://arxiv.org/pdf/1905.01596v1.pdf | |
| PWC | https://paperswithcode.com/paper/fast-communication-efficient-spectral |
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Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
| Title | Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning |
| Authors | Yuan Lin, John McPhee, Nasser L. Azad |
| Abstract | The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions. |
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| Published | 2019-05-07 |
| URL | https://arxiv.org/abs/1905.08314v2 |
| https://arxiv.org/pdf/1905.08314v2.pdf | |
| PWC | https://paperswithcode.com/paper/190508314 |
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Address Instance-level Label Prediction in Multiple Instance Learning
| Title | Address Instance-level Label Prediction in Multiple Instance Learning |
| Authors | Minlong Peng, Qi Zhang |
| Abstract | \textit{Multiple Instance Learning} (MIL) is concerned with learning from bags of instances, where only bag labels are given and instance labels are unknown. Existent approaches in this field were mainly designed for the bag-level label prediction (predict labels for bags) but not the instance-level (predict labels for instances), with the task loss being only defined at the bag level. This restricts their application in many tasks, where the instance-level labels are more interested. In this paper, we propose a novel algorithm, whose loss is specifically defined at the instance level, to address instance-level label prediction in MIL. We prove that the loss of this algorithm can be unbiasedly and consistently estimated without using instance labels, under the i.i.d assumption. Empirical study validates the above statements and shows that the proposed algorithm can achieve superior instance-level and comparative bag-level performance, compared to state-of-the-art MIL methods. In addition, it shows that the proposed method can achieve similar results as the fully supervised model (trained with instance labels) for label prediction at the instance level. |
| Tasks | Multiple Instance Learning |
| Published | 2019-05-29 |
| URL | https://arxiv.org/abs/1905.12226v1 |
| https://arxiv.org/pdf/1905.12226v1.pdf | |
| PWC | https://paperswithcode.com/paper/address-instance-level-label-prediction-in |
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Learning the Wireless V2I Channels Using Deep Neural Networks
| Title | Learning the Wireless V2I Channels Using Deep Neural Networks |
| Authors | Tian-Hao Li, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong, Risala T. Khan |
| Abstract | For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance. |
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| Published | 2019-07-10 |
| URL | https://arxiv.org/abs/1907.04831v1 |
| https://arxiv.org/pdf/1907.04831v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-the-wireless-v2i-channels-using-deep |
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Reinforcement Learning without Ground-Truth State
| Title | Reinforcement Learning without Ground-Truth State |
| Authors | Xingyu Lin, Harjatin Singh Baweja, David Held |
| Abstract | To perform robot manipulation tasks, a low-dimensional state of the environment typically needs to be estimated. However, designing a state estimator can sometimes be difficult, especially in environments with deformable objects. An alternative is to learn an end-to-end policy that maps directly from high-dimensional sensor inputs to actions. However, if this policy is trained with reinforcement learning, then without a state estimator, it is hard to specify a reward function based on high-dimensional observations. To meet this challenge, we propose a simple indicator reward function for goal-conditioned reinforcement learning: we only give a positive reward when the robot’s observation exactly matches a target goal observation. We show that by relabeling the original goal with the achieved goal to obtain positive rewards (Andrychowicz et al., 2017), we can learn with the indicator reward function even in continuous state spaces. We propose two methods to further speed up convergence with indicator rewards: reward balancing and reward filtering. We show comparable performance between our method and an oracle which uses the ground-truth state for computing rewards. We show that our method can perform complex tasks in continuous state spaces such as rope manipulation from RGB-D images, without knowledge of the ground-truth state. |
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| Published | 2019-05-20 |
| URL | https://arxiv.org/abs/1905.07866v2 |
| https://arxiv.org/pdf/1905.07866v2.pdf | |
| PWC | https://paperswithcode.com/paper/reinforcement-learning-without-ground-truth |
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A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images
| Title | A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images |
| Authors | David Dov, Shahar Ziv Kovalsky, Jonathan Cohen, Danielle Elliott Range, Ricardo Henao, Lawrence Carin |
| Abstract | We consider thyroid-malignancy prediction from ultra-high-resolution whole-slide cytopathology images. We propose a deep-learning-based algorithm that is inspired by the way a cytopathologist diagnoses the slides. The algorithm identifies diagnostically relevant image regions and assigns them local malignancy scores, that in turn are incorporated into a global malignancy prediction. We discuss the relation of our deep-learning-based approach to multiple-instance learning (MIL) and describe how it deviates from classical MIL methods by the use of a supervised procedure to extract relevant regions from the whole-slide. The analysis of our algorithm further reveals a close relation to hypothesis testing, which, along with unique characteristics of thyroid cytopathology, allows us to devise an improved training strategy. We further propose an ordinal regression framework for the simultaneous prediction of thyroid malignancy and an ordered diagnostic score acting as a regularizer, which further improves the predictions of the network. Experimental results demonstrate that the proposed algorithm outperforms several competing methods, achieving performance comparable to human experts. |
| Tasks | Multiple Instance Learning |
| Published | 2019-04-26 |
| URL | http://arxiv.org/abs/1904.12739v1 |
| http://arxiv.org/pdf/1904.12739v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-deep-learning-algorithm-for-thyroid |
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Large Random Forests: Optimisation for Rapid Evaluation
| Title | Large Random Forests: Optimisation for Rapid Evaluation |
| Authors | Frederik Gossen, Bernhard Steffen |
| Abstract | Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise is the outcome of their predictions. However, this comes at a cost: their running time for classification grows linearly with the number of trees, i.e. the size of the forest. In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram. Our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure. |
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| Published | 2019-12-23 |
| URL | https://arxiv.org/abs/1912.10934v1 |
| https://arxiv.org/pdf/1912.10934v1.pdf | |
| PWC | https://paperswithcode.com/paper/large-random-forests-optimisation-for-rapid |
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Evaluation Framework of Superpixel Methods with a Global Regularity Measure
| Title | Evaluation Framework of Superpixel Methods with a Global Regularity Measure |
| Authors | Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis |
| Abstract | In the superpixel literature, the comparison of state-of-the-art methods can be biased by the non-robustness of some metrics to decomposition aspects, such as the superpixel scale. Moreover, most recent decomposition methods allow to set a shape regularity parameter, which can have a substantial impact on the measured performances. In this paper, we introduce an evaluation framework, that aims to unify the comparison process of superpixel methods. We investigate the limitations of existing metrics, and propose to evaluate each of the three core decomposition aspects: color homogeneity, respect of image objects and shape regularity. To measure the regularity aspect, we propose a new global regularity measure (GR), which addresses the non-robustness of state-of-the-art metrics. We evaluate recent superpixel methods with these criteria, at several superpixel scales and regularity levels. The proposed framework reduces the bias in the comparison process of state-of-the-art superpixel methods. Finally, we demonstrate that the proposed GR measure is correlated with the performances of various applications. |
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| Published | 2019-03-17 |
| URL | http://arxiv.org/abs/1903.07162v1 |
| http://arxiv.org/pdf/1903.07162v1.pdf | |
| PWC | https://paperswithcode.com/paper/evaluation-framework-of-superpixel-methods |
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Robust Knowledge Discovery via Low-rank Modeling
| Title | Robust Knowledge Discovery via Low-rank Modeling |
| Authors | Zhengming Ding, Ming Shao |
| Abstract | It is always an attractive task to discover knowledge for various learning problems; however, this knowledge discovery and maintenance process usually suffers from noise, incompleteness or knowledge domain mismatch. Thus, robust knowledge discovery by removing the noisy features or samples, complementing incomplete data, and mitigating the distribution difference becomes the key. Along this line of research, low-rank modeling is widely-used to solve these challenges. This survey covers the topic of: (1) robust knowledge recovery, (2) robust knowledge transfer, (3) robust knowledge fusion, centered around several major applications. First of all, we deliver a unified formulation for robust knowledge discovery based on a given dataset. Second, we discuss robust knowledge transfer and fusion given multiple datasets with different knowledge flows, followed by practical challenges, model variations, and remarks. Finally, we highlight future research of robust knowledge discovery for incomplete, unbalance, large-scale data analysis. This would benefit AI community from literature review to future direction. |
| Tasks | Transfer Learning |
| Published | 2019-09-28 |
| URL | https://arxiv.org/abs/1909.13123v1 |
| https://arxiv.org/pdf/1909.13123v1.pdf | |
| PWC | https://paperswithcode.com/paper/robust-knowledge-discovery-via-low-rank |
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Reflecting After Learning for Understanding
| Title | Reflecting After Learning for Understanding |
| Authors | Lee Martie, Mohammad Arif Ul Alam, Gaoyuan Zhang, Ryan R. Anderson |
| Abstract | Today, image classification is a common way for systems to process visual content. Although neural network approaches to classification have seen great progress in reducing error rates, it is not clear what this means for a cognitive system that needs to make sense of the multiple and competing predictions from its own classifiers. As a step to address this, we present a novel framework that uses meta-reasoning and meta-operations to unify predictions into abstractions, properties, or relationships. Using the framework on images from ImageNet, we demonstrate systems that unify 41% to 46% of predictions in general and unify 67% to 75% of predictions when the systems can explain their conceptual differences. We also demonstrate a system in “the wild” by feeding live video images through it and show it unifying 51% of predictions in general and 69% of predictions when their differences can be explained conceptually by the system. In a survey given to 24 participants, we found that 87% of the unified predictions describe their corresponding images. |
| Tasks | Image Classification |
| Published | 2019-10-18 |
| URL | https://arxiv.org/abs/1910.08243v1 |
| https://arxiv.org/pdf/1910.08243v1.pdf | |
| PWC | https://paperswithcode.com/paper/reflecting-after-learning-for-understanding |
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Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification
| Title | Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification |
| Authors | Tu Vu, Mohit Iyyer |
| Abstract | While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque. In this paper, we investigate a state-of-the-art paragraph embedding method proposed by Zhang et al. (2017) and discover that it cannot reliably tell whether a given sentence occurs in the input paragraph or not. We formulate a sentence content task to probe for this basic linguistic property and find that even a much simpler bag-of-words method has no trouble solving it. This result motivates us to replace the reconstruction-based objective of Zhang et al. (2017) with our sentence content probe objective in a semi-supervised setting. Despite its simplicity, our objective improves over paragraph reconstruction in terms of (1) downstream classification accuracies on benchmark datasets, (2) faster training, and (3) better generalization ability. |
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| Published | 2019-06-09 |
| URL | https://arxiv.org/abs/1906.03656v1 |
| https://arxiv.org/pdf/1906.03656v1.pdf | |
| PWC | https://paperswithcode.com/paper/encouraging-paragraph-embeddings-to-remember |
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