January 28, 2020

3429 words 17 mins read

Paper Group ANR 1067

Knowledge-guided Text Structuring in Clinical Trials. Machine Learning-enhanced Realistic Framework for Real-time Seismic Monitoring – The Winning Solution of the 2017 International Aftershock Detection Contest. Machine Truth Serum. Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications. High-Resolutio …

Knowledge-guided Text Structuring in Clinical Trials


Title	Knowledge-guided Text Structuring in Clinical Trials
Authors	Yingcheng Sun, Kenneth Loparo
Abstract	Clinical trial records are variable resources or the analysis of patients and diseases. Information extraction from free text such as eligibility criteria and summary of results and conclusions in clinical trials would better support computer-based eligibility query formulation and electronic patient screening. Previous research has focused on extracting information from eligibility criteria, with usually a single pair of medical entity and attribute, but seldom considering other kinds of free text with multiple entities, attributes and relations that are more complex for parsing. In this paper, we propose a knowledge-guided text structuring framework with an automatically generated knowledge base as training corpus and word dependency relations as context information to transfer free text into formal, computer-interpretable representations. Experimental results show that our method can achieve overall high precision and recall, demonstrating the effectiveness and efficiency of the proposed method.
Tasks
Published	2019-12-28
URL	https://arxiv.org/abs/1912.12380v1
PDF	https://arxiv.org/pdf/1912.12380v1.pdf
PWC	https://paperswithcode.com/paper/knowledge-guided-text-structuring-in-clinical
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Machine Learning-enhanced Realistic Framework for Real-time Seismic Monitoring – The Winning Solution of the 2017 International Aftershock Detection Contest


Title	Machine Learning-enhanced Realistic Framework for Real-time Seismic Monitoring – The Winning Solution of the 2017 International Aftershock Detection Contest
Authors	Dazhong Shen, Qi Zhang, Tong Xu, Hengshu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, Lihua Fang, Enhong Chen, Hui Xiong
Abstract	Identifying the arrival times of seismic P-phases plays a significant role in real-time seismic monitoring, which provides critical guidance for emergency response activities. While considerable research has been conducted on this topic, efficiently capturing the arrival times of seismic P-phases hidden within intensively distributed and noisy seismic waves, such as those generated by the aftershocks of destructive earthquakes, remains a real challenge since existing methods rely on laborious expert supervision. To this end, in this paper, we present a machine learning-enhanced framework, ML-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms. More specifically, ML-Picker consists of three modules, namely, Trigger, Classifier, and Refiner, and an ensemble learning strategy is exploited to integrate several machine learning classifiers. An evaluation of the aftershocks following the $M8.0$ Wenchuan earthquake demonstrates that ML-Picker can not only achieve the best identification performance but also identify 120% more seismic P-phase arrivals as complementary data. Meanwhile, experimental results also reveal both the applicability of different machine learning models for waveforms collected from different seismic stations and the regularities of seismic P-phase arrivals that might be neglected during manual inspection. These findings clearly validate the effectiveness, efficiency, flexibility and stability of ML-Picker. In particular, with the preliminary version of ML-Picker, we won the championship in the First Season and were the runner-up in the Finals of the 2017 International Aftershock Detection Contest hosted by the China Earthquake Administration, in which 1,143 teams participated from around the world.
Tasks
Published	2019-11-21
URL	https://arxiv.org/abs/1911.09275v1
PDF	https://arxiv.org/pdf/1911.09275v1.pdf
PWC	https://paperswithcode.com/paper/machine-learning-enhanced-realistic-framework
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Machine Truth Serum


Title	Machine Truth Serum
Authors	Tianyi Luo, Yang Liu
Abstract	Wisdom of the crowd revealed a striking fact that the majority answer from a crowd is often more accurate than any individual expert. We observed the same story in machine learning–ensemble methods leverage this idea to combine multiple learning algorithms to obtain better classification performance. Among many popular examples is the celebrated Random Forest, which applies the majority voting rule in aggregating different decision trees to make the final prediction. Nonetheless, these aggregation rules would fail when the majority is more likely to be wrong. In this paper, we extend the idea proposed in Bayesian Truth Serum that “a surprisingly more popular answer is more likely the true answer” to classification problems. The challenge for us is to define or detect when an answer should be considered as being “surprising”. We present two machine learning aided methods which aim to reveal the truth when it is minority instead of majority who has the true answer. Our experiments over real-world datasets show that better classification performance can be obtained compared to always trusting the majority voting. Our proposed methods also outperform popular ensemble algorithms. Our approach can be generically applied as a subroutine in ensemble methods to replace majority voting rule.
Tasks
Published	2019-09-28
URL	https://arxiv.org/abs/1909.13004v1
PDF	https://arxiv.org/pdf/1909.13004v1.pdf
PWC	https://paperswithcode.com/paper/machine-truth-serum
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Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications


Title	Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
Authors	David Ruhe, Giovanni Cinà, Michele Tonutti, Daan de Bruin, Paul Elbers
Abstract	The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learning models usually only provide point-estimates and no uncertainty of predictions. In practice, uncertain predictions should be presented to doctors with extra care in order to prevent potentially catastrophic treatment decisions. In this work we show how Bayesian modelling and the predictive uncertainty that it provides can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting. We derive analytically a bound on the prediction loss with respect to predictive uncertainty. The bound shows that uncertainty can mitigate loss. Furthermore, we apply a Bayesian Neural Network to the MIMIC-III dataset, predicting risk of mortality of ICU patients. Our empirical results show that uncertainty can indeed prevent potential errors and reliably identifies out-of-domain patients. These results suggest that Bayesian predictive uncertainty can greatly improve trustworthiness of machine learning models in high-risk settings such as the ICU.
Tasks	Decision Making
Published	2019-06-20
URL	https://arxiv.org/abs/1906.08619v1
PDF	https://arxiv.org/pdf/1906.08619v1.pdf
PWC	https://paperswithcode.com/paper/bayesian-modelling-in-practice-using
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High-Resolution Road Vehicle Collision Prediction for the City of Montreal


Title	High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Authors	Antoine Hébert, Timothée Guédon, Tristan Glatard, Brigitte Jaumard
Abstract	Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility.
Tasks
Published	2019-05-21
URL	https://arxiv.org/abs/1905.08770v3
PDF	https://arxiv.org/pdf/1905.08770v3.pdf
PWC	https://paperswithcode.com/paper/high-resolution-road-vehicle-collision
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Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification


Title	Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
Authors	Chen Wang, Qin Yu, Ruisen Luo, Dafeng Hui, Kai Zhou, Yanmei Yu, Chao Sun, Xiaofeng Gong
Abstract	Dynamic ensembling of classifiers is an effective approach in processing label-imbalanced classifications. However, in dynamic ensemble methods, the combination of classifiers is usually determined by the local competence and conventional regularization methods are difficult to apply, leaving the technique prone to overfitting. In this paper, focusing on the binary label-imbalanced classification field, a novel method of Adaptive Ensemble of classifiers with Regularization (AER) has been proposed. The method deals with the overfitting problem from a perspective of implicit regularization. Specifically, it leverages the properties of Stochastic Gradient Descent (SGD) to obtain the solution with the minimum norm to achieve regularization, and interpolates ensemble weights via the global geometry of data to further prevent overfitting. The method enjoys a favorable time and memory complexity, and theoretical proofs show that algorithms implemented with AER paradigm have time and memory complexities upper-bounded by their original implementations. Furthermore, the proposed AER method is tested with a specific implementation based on Gradient Boosting Machine (XGBoost) on the three datasets: UCI Bioassay, KEEL Abalone19, and a set of GMM-sampled artificial dataset. Results show that the proposed AER algorithm can outperform the major existing algorithms based on multiple metrics, and Mcnemar’s tests are applied to validate performance superiorities. To summarize, this work complements regularization for dynamic ensemble methods and develops an algorithm superior in grasping both the global and local geometry of data to alleviate overfitting in imbalanced data classification.
Tasks
Published	2019-08-09
URL	https://arxiv.org/abs/1908.03595v2
PDF	https://arxiv.org/pdf/1908.03595v2.pdf
PWC	https://paperswithcode.com/paper/adaptive-ensemble-of-classifiers-with
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Monocular Outdoor Semantic Mapping with a Multi-task Network


Title	Monocular Outdoor Semantic Mapping with a Multi-task Network
Authors	Yucai Bai, Lei Fan, Ziyu Pan, Long Chen
Abstract	In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is still challenging to produce a dense outdoor semantic map from a monocular image stream. Motivated by this target, in this paper, we propose a method for large-scale 3D reconstruction from consecutive monocular images. First, with the correlation of underlying information between depth and semantic prediction, a novel multi-task Convolutional Neural Network (CNN) is designed for joint prediction. Given a single image, the network learns low-level information with a shared encoder and separately predicts with decoders containing additional Atrous Spatial Pyramid Pooling (ASPP) layers and the residual connection which merits disparities and semantic mutually. To overcome the inconsistency of monocular depth prediction for reconstruction, post-processing steps with the superpixelization and the effective 3D representation approach are obtained to give the final semantic map. Experiments are compared with other methods on both semantic labeling and depth prediction. We also qualitatively demonstrate the map reconstructed from large-scale, difficult monocular image sequences to prove the effectiveness and superiority.
Tasks	3D Reconstruction, Autonomous Driving, Depth Estimation
Published	2019-01-17
URL	https://arxiv.org/abs/1901.05807v3
PDF	https://arxiv.org/pdf/1901.05807v3.pdf
PWC	https://paperswithcode.com/paper/towards-building-the-semantic-map-from-a
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Title	MMKG: Multi-Modal Knowledge Graphs
Authors	Ye Liu, Hui Li, Alberto Garcia-Duran, Mathias Niepert, Daniel Onoro-Rubio, David S. Rosenblum
Abstract	We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs.We validate the utility ofMMKG in the sameAs link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.
Tasks	Knowledge Graphs, Link Prediction
Published	2019-03-13
URL	http://arxiv.org/abs/1903.05485v1
PDF	http://arxiv.org/pdf/1903.05485v1.pdf
PWC	https://paperswithcode.com/paper/mmkg-multi-modal-knowledge-graphs
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Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery


Title	Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Authors	Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell
Abstract	This paper develops a new class of nonconvex regularizers for low-rank matrix recovery. Many regularizers are motivated as convex relaxations of the matrix rank function. Our new factor group-sparse regularizers are motivated as a relaxation of the number of nonzero columns in a factorization of the matrix. These nonconvex regularizers are sharper than the nuclear norm; indeed, we show they are related to Schatten-$p$ norms with arbitrarily small $0 < p \leq 1$. Moreover, these factor group-sparse regularizers can be written in a factored form that enables efficient and effective nonconvex optimization; notably, the method does not use singular value decomposition. We provide generalization error bounds for low-rank matrix completion which show improved upper bounds for Schatten-$p$ norm reglarization as $p$ decreases. Compared to the max norm and the factored formulation of the nuclear norm, factor group-sparse regularizers are more efficient, accurate, and robust to the initial guess of rank. Experiments show promising performance of factor group-sparse regularization for low-rank matrix completion and robust principal component analysis.
Tasks	Low-Rank Matrix Completion, Matrix Completion
Published	2019-11-13
URL	https://arxiv.org/abs/1911.05774v2
PDF	https://arxiv.org/pdf/1911.05774v2.pdf
PWC	https://paperswithcode.com/paper/factor-group-sparse-regularization-for-1
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Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning


Title	Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning
Authors	Mathews Jacob, Merry P. Mani, Jong Chul Ye
Abstract	In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation. This framework is centered on the fundamental duality between the compactness (e.g., sparsity) of the continuous signal and the rank of a structured matrix, whose entries are functions of the signal. This property enables the reformulation of the signal recovery as a low-rank structured matrix completion, which comes with performance guarantees. We will also review fast algorithms that are comparable in complexity to current compressed sensing methods, which enables the application of the framework to large-scale magnetic resonance (MR) recovery problems. The remarkable flexibility of the formulation can be used to exploit signal properties that are difficult to capture by current sparse and low-rank optimization strategies. We demonstrate the utility of the framework in a wide range of MR imaging (MRI) applications, including highly accelerated imaging, calibration-free acquisition, MR artifact correction, and ungated dynamic MRI.
Tasks	Calibration, Low-Rank Matrix Completion, Matrix Completion
Published	2019-10-27
URL	https://arxiv.org/abs/1910.12162v1
PDF	https://arxiv.org/pdf/1910.12162v1.pdf
PWC	https://paperswithcode.com/paper/structured-low-rank-algorithms-theory-mr
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NRMVS: Non-Rigid Multi-View Stereo


Title	NRMVS: Non-Rigid Multi-View Stereo
Authors	Matthias Innmann, Kihwan Kim, Jinwei Gu, Matthias Niessner, Charles Loop, Marc Stamminger, Jan Kautz
Abstract	Scene reconstruction from unorganized RGB images is an important task in many computer vision applications. Multi-view Stereo (MVS) is a common solution in photogrammetry applications for the dense reconstruction of a static scene. The static scene assumption, however, limits the general applicability of MVS algorithms, as many day-to-day scenes undergo non-rigid motion, e.g., clothes, faces, or human bodies. In this paper, we open up a new challenging direction: dense 3D reconstruction of scenes with non-rigid changes observed from arbitrary, sparse, and wide-baseline views. We formulate the problem as a joint optimization of deformation and depth estimation, using deformation graphs as the underlying representation. We propose a new sparse 3D to 2D matching technique, together with a dense patch-match evaluation scheme to estimate deformation and depth with photometric consistency. We show that creating a dense 4D structure from a few RGB images with non-rigid changes is possible, and demonstrate that our method can be used to interpolate novel deformed scenes from various combinations of these deformation estimates derived from the sparse views.
Tasks	3D Reconstruction, Depth Estimation
Published	2019-01-12
URL	http://arxiv.org/abs/1901.03910v1
PDF	http://arxiv.org/pdf/1901.03910v1.pdf
PWC	https://paperswithcode.com/paper/nrmvs-non-rigid-multi-view-stereo
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Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning


Title	Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Authors	Soumyasundar Pal, Florence Regol, Mark Coates
Abstract	Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings. Despite their impressive performance, the techniques have a limited capability to incorporate the uncertainty in the underlined graph structure. In order to address this issue, a Bayesian GCNN (BGCN) framework was recently proposed. In this framework, the observed graph is considered to be a random realization from a parametric random graph model and the joint Bayesian inference of the graph and GCNN weights is performed. In this paper, we propose a non-parametric generative model for graphs and incorporate it within the BGCN framework. In addition to the observed graph, our approach effectively uses the node features and training labels in the posterior inference of graphs and attains superior or comparable performance in benchmark node classification tasks.
Tasks	Bayesian Inference, Graph Classification, Matrix Completion, Node Classification
Published	2019-10-26
URL	https://arxiv.org/abs/1910.12132v1
PDF	https://arxiv.org/pdf/1910.12132v1.pdf
PWC	https://paperswithcode.com/paper/bayesian-graph-convolutional-neural-networks-1
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Exploring Deep Spiking Neural Networks for Automated Driving Applications


Title	Exploring Deep Spiking Neural Networks for Automated Driving Applications
Authors	Sambit Mohapatra, Heinrich Gotzig, Senthil Yogamani, Stefan Milz, Raoul Zollner
Abstract	Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rapid progress in embedded processors, power consumption and cost is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing to achieve low-power event-driven hardware architecture which has a potential for high efficiency. In this paper, we explore the role of deep spiking neural networks (SNN) for automated driving applications. We provide an overview of progress on SNN and argue how it can be a good fit for automated driving applications.
Tasks	Depth Estimation, Object Detection, Semantic Segmentation, Visual Odometry
Published	2019-01-11
URL	http://arxiv.org/abs/1903.02080v1
PDF	http://arxiv.org/pdf/1903.02080v1.pdf
PWC	https://paperswithcode.com/paper/exploring-deep-spiking-neural-networks-for
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Link Prediction via Higher-Order Motif Features


Title	Link Prediction via Higher-Order Motif Features
Authors	Ghadeer Abuoda, Gianmarco De Francisci Morales, Ashraf Aboulnaga
Abstract	Link prediction requires predicting which new links are likely to appear in a graph. Being able to predict unseen links with good accuracy has important applications in several domains such as social media, security, transportation, and recommendation systems. A common approach is to use features based on the common neighbors of an unconnected pair of nodes to predict whether the pair will form a link in the future. In this paper, we present an approach for link prediction that relies on higher-order analysis of the graph topology, well beyond common neighbors. We treat the link prediction problem as a supervised classification problem, and we propose a set of features that depend on the patterns or motifs that a pair of nodes occurs in. By using motifs of sizes 3, 4, and 5, our approach captures a high level of detail about the graph topology within the neighborhood of the pair of nodes, which leads to a higher classification accuracy. In addition to proposing the use of motif-based features, we also propose two optimizations related to constructing the classification dataset from the graph. First, to ensure that positive and negative examples are treated equally when extracting features, we propose adding the negative examples to the graph as an alternative to the common approach of removing the positive ones. Second, we show that it is important to control for the shortest-path distance when sampling pairs of nodes to form negative examples, since the difficulty of prediction varies with the shortest-path distance. We experimentally demonstrate that using off-the-shelf classifiers with a well constructed classification dataset results in up to 10 percentage points increase in accuracy over prior topology-based and feature learning methods.
Tasks	Link Prediction, Recommendation Systems
Published	2019-02-08
URL	http://arxiv.org/abs/1902.06679v1
PDF	http://arxiv.org/pdf/1902.06679v1.pdf
PWC	https://paperswithcode.com/paper/link-prediction-via-higher-order-motif
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Title	RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
Authors	Humam Alwassel, Alejandro Pardo, Fabian Caba Heilbron, Ali Thabet, Bernard Ghanem
Abstract	Video action detectors are usually trained using datasets with fully-supervised temporal annotations. Building such datasets is an expensive task. To alleviate this problem, recent methods have tried to leverage weak labelling, where videos are untrimmed and only a video-level label is available. In this paper, we propose RefineLoc, a new weakly-supervised temporal action localization method. RefineLoc uses an iterative refinement approach by estimating and training on snippet-level pseudo ground truth at every iteration. We show the benefit of this iterative approach and present an extensive analysis of different pseudo ground truth generators. We show the effectiveness of our model on two standard action datasets, ActivityNet v1.2 and THUMOS14. RefineLoc equipped with a segment prediction-based pseudo ground truth generator improves the state-of-the-art in weakly-supervised temporal localization on the challenging and large-scale ActivityNet dataset by 1.5% in average mAP.
Tasks	Action Localization, Temporal Action Localization, Temporal Localization, Weakly Supervised Action Localization, Weakly-supervised Temporal Action Localization
Published	2019-03-30
URL	https://arxiv.org/abs/1904.00227v2
PDF	https://arxiv.org/pdf/1904.00227v2.pdf
PWC	https://paperswithcode.com/paper/refineloc-iterative-refinement-for-weakly
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