January 27, 2020

3170 words 15 mins read

Paper Group ANR 1272

Observing LOD using Equivalent Set Graphs: it is mostly flat and sparsely linked. Multi-Objective Evolutionary Algorithms platform with support for flexible hybridization tools. Measuring Belief and Risk Attitude. Evaluating robustness of language models for chief complaint extraction from patient-generated text. Safe and Near-Optimal Policy Learni …

Observing LOD using Equivalent Set Graphs: it is mostly flat and sparsely linked


Title	Observing LOD using Equivalent Set Graphs: it is mostly flat and sparsely linked
Authors	Luigi Asprino, Wouter Beek, Paolo Ciancarini, Frank van Harmelen, Valentina Presutti
Abstract	This paper presents an empirical study aiming at understanding the modeling style and the overall semantic structure of Linked Open Data. We observe how classes, properties and individuals are used in practice. We also investigate how hierarchies of concepts are structured, and how much they are linked. In addition to discussing the results, this paper contributes (i) a conceptual framework, including a set of metrics, which generalises over the observable constructs; (ii) an open source implementation that facilitates its application to other Linked Data knowledge graphs.
Tasks	Knowledge Graphs
Published	2019-06-19
URL	https://arxiv.org/abs/1906.08097v3
PDF	https://arxiv.org/pdf/1906.08097v3.pdf
PWC	https://paperswithcode.com/paper/the-linked-open-data-cloud-is-more-abstract
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Multi-Objective Evolutionary Algorithms platform with support for flexible hybridization tools


Title	Multi-Objective Evolutionary Algorithms platform with support for flexible hybridization tools
Authors	Michał Idzik
Abstract	Working with complex, high-level MOEA meta-models such as Multiobjec-tive Optimization Hierarchic Genetic Strategy (MO-mHGS) with multi-deme support usually requires dedicated implementation and configuration for each internal (single-deme) algorithm variant. If we generalize meta-model, we can simplify whole simulation process and bind any internal algorithm (we denote it as a driver), without providing redundant meta-model implementations. This idea has become a fundamental of Evogil platform. Our aim was to allow construct-ing custom hybrid models or combine existing solutions in runtime simulation environment. We define hybrid solution as a composition of a meta-model and a driver (or multiple drivers). Meta-model uses drivers to perform evolutionary calculations and process their results. Moreover, Evogil provides set of ready-made solutions divided into two groups (multi-deme meta-models and single-deme drivers), as well as processing tools (quality metrics, statistics and plotting scripts), simulation management and results persistence layer.
Tasks
Published	2019-12-16
URL	https://arxiv.org/abs/1912.07319v1
PDF	https://arxiv.org/pdf/1912.07319v1.pdf
PWC	https://paperswithcode.com/paper/multi-objective-evolutionary-algorithms
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Measuring Belief and Risk Attitude


Title	Measuring Belief and Risk Attitude
Authors	Sven Neth
Abstract	Ramsey (1926) sketches a proposal for measuring the subjective probabilities of an agent by their observable preferences, assuming that the agent is an expected utility maximizer. I show how to extend the spirit of Ramsey’s method to a strictly wider class of agents: risk-weighted expected utility maximizers (Buchak 2013). In particular, I show how we can measure the risk attitudes of an agent by their observable preferences, assuming that the agent is a risk-weighted expected utility maximizer. Further, we can leverage this method to measure the subjective probabilities of a risk-weighted expected utility maximizer.
Tasks
Published	2019-07-22
URL	https://arxiv.org/abs/1907.09115v1
PDF	https://arxiv.org/pdf/1907.09115v1.pdf
PWC	https://paperswithcode.com/paper/measuring-belief-and-risk-attitude
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Evaluating robustness of language models for chief complaint extraction from patient-generated text


Title	Evaluating robustness of language models for chief complaint extraction from patient-generated text
Authors	Ilya Valmianski, Caleb Goodwin, Ian M. Finn, Naqi Khan, Daniel S. Zisook
Abstract	Automated classification of chief complaints from patient-generated text is a critical first step in developing scalable platforms to triage patients without human intervention. In this work, we evaluate several approaches to chief complaint classification using a novel Chief Complaint (CC) Dataset that contains ~200,000 patient-generated reasons-for-visit entries mapped to a set of 795 discrete chief complaints. We examine the use of several fine-tuned bidirectional transformer (BERT) models trained on both unrelated texts as well as on the CC dataset. We contrast this performance with a TF-IDF baseline. Our evaluation has three components: (1) a random test hold-out from the original dataset; (2) a “misspelling set,” consisting of a hand-selected subset of the test set, where every entry has at least one misspelling; (3) a separate experimenter-generated free-text set. We find that the TF-IDF model performs significantly better than the strongest BERT-based model on the test (best BERT PR-AUC $0.3597 \pm 0.0041$ vs TF-IDF PR-AUC $0.3878 \pm 0.0148$, $p=7\cdot 10^{-5}$), and is statistically comparable to the misspelling sets (best BERT PR-AUC $0.2579 \pm 0.0079$ vs TF-IDF PR-AUC $0.2733 \pm 0.0130$, $p=0.06$). However, when examining model predictions on experimenter-generated queries, some concerns arise about TF-IDF baseline’s robustness. Our results suggest that in certain tasks, simple language embedding baselines may be very performant; however, truly understanding their robustness requires further analysis.
Tasks
Published	2019-11-15
URL	https://arxiv.org/abs/1911.06915v1
PDF	https://arxiv.org/pdf/1911.06915v1.pdf
PWC	https://paperswithcode.com/paper/evaluating-robustness-of-language-models-for
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Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks


Title	Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks
Authors	Xiaojing Zhang, Monimoy Bujarbaruah, Francesco Borrelli
Abstract	In this paper, we propose a novel framework for approximating the explicit MPC law for linear parameter-varying systems using supervised learning. In contrast to most existing approaches, we not only learn the control policy, but also a “certificate policy”, that allows us to estimate the sub-optimality of the learned control policy online, during execution-time. We learn both these policies from data using supervised learning techniques, and also provide a randomized method that allows us to guarantee the quality of each learned policy, measured in terms of feasibility and optimality. This in turn allows us to bound the probability of the learned control policy of being infeasible or suboptimal, where the check is performed by the certificate policy. Since our algorithm does not require the solution of an optimization problem during run-time, it can be deployed even on resource-constrained systems. We illustrate the efficacy of the proposed framework on a vehicle dynamics control problem where we demonstrate a speedup of up to two orders of magnitude compared to online optimization with minimal performance degradation.
Tasks
Published	2019-06-19
URL	https://arxiv.org/abs/1906.08257v1
PDF	https://arxiv.org/pdf/1906.08257v1.pdf
PWC	https://paperswithcode.com/paper/safe-and-near-optimal-policy-learning-for
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NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution


Title	NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
Authors	Aditya Khandelwal, Suraj Sawant
Abstract	Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: simple rule-based systems, Machine Learning classifiers, Conditional Random Field Models, CNNs and more recently BiLSTMs. In this paper, we look at applying Transfer Learning to this problem. First, we extensively review previous literature addressing Negation Detection and Scope Resolution across the 3 datasets that have gained popularity over the years: BioScope Corpus, the Sherlock dataset, and the SFU Review Corpus. We then explore the decision choices involved with using BERT, a popular transfer learning model, for this task, and report a new state-of-the-art for scope resolution across all 3 datasets. Our model, referred to as NegBERT, achieves a token level F1 score on scope resolution of 92.36 on the Sher-lock dataset, 95.68 on the BioScope Abstracts, 91.24 on the BioScope Full Papers, 90.95 on the SFU dataset, out-performing the previous state-of-the-art by a significant margin. We also analyze the model’s generalizability to datasets on which it is not trained.
Tasks	Negation Detection, Negation Scope Resolution, Transfer Learning
Published	2019-11-11
URL	https://arxiv.org/abs/1911.04211v3
PDF	https://arxiv.org/pdf/1911.04211v3.pdf
PWC	https://paperswithcode.com/paper/negbert-a-transfer-learning-approach-for
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Efficient Bayesian synthetic likelihood with whitening transformations


Title	Efficient Bayesian synthetic likelihood with whitening transformations
Authors	Jacob W. Priddle, Scott A. Sisson, David T. Frazier, Christopher Drovandi
Abstract	Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions. However, they can be computationally demanding. Bayesian synthetic likelihood (BSL) is a popular such method that approximates the likelihood function of the summary statistic with a known, tractable distribution – typically Gaussian – and then performs statistical inference using standard likelihood-based techniques. However, as the number of summary statistics grows, the number of model simulations required to accurately estimate the covariance matrix for this likelihood rapidly increases. This poses significant challenge for the application of BSL, especially in cases where model simulation is expensive. In this article we propose whitening BSL (wBSL) – an efficient BSL method that uses approximate whitening transformations to decorrelate the summary statistics at each algorithm iteration. We show empirically that this can reduce the number of model simulations required to implement BSL by more than an order of magnitude, without much loss of accuracy. We explore a range of whitening procedures and demonstrate the performance of wBSL on a range of simulated and real modelling scenarios from ecology and biology.
Tasks	Bayesian Inference
Published	2019-09-11
URL	https://arxiv.org/abs/1909.04857v2
PDF	https://arxiv.org/pdf/1909.04857v2.pdf
PWC	https://paperswithcode.com/paper/efficient-bayesian-synthetic-likelihood-with
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Stable Matrix Completion using Properly Configured Kronecker Product Decomposition


Title	Stable Matrix Completion using Properly Configured Kronecker Product Decomposition
Authors	Chencheng Cai, Rong Chen, Han Xiao
Abstract	Matrix completion problems are the problems of recovering missing entries in a partially observed high dimensional matrix with or without noise. Such a problem is encountered in a wide range of applications such as collaborative filtering, global positioning and remote sensing. Most of the existing matrix completion algorithms assume a low rank structure of the underlying complete matrix and perform reconstruction through the recovery of the low-rank structure using singular value decomposition. In this paper, we propose an alternative and more flexible structure for the underlying true complete matrix for the purpose of matrix completion and denoising. Specifically, instead of assuming a low matrix rank, we assume the underlying complete matrix has a low Kronecker product rank structure. Such a structure is often seen in the matrix observations in signal processing and image processing applications. The Kronecker product structure also includes low rank singular value decomposition structure commonly used as one of its special cases. The extra flexibility assumed for the underlying structure allows for using much less number of parameters but also raises the challenge of determining the proper Kronecker product configuration to be used. In this article, we propose to use a class of information criteria for the determination of the proper configuration and study its empirical performance in matrix completion problems. Simulation studies show promising results that the true underlying configuration can be accurately selected by the information criteria and the accompanying matrix completion algorithm can produce more accurate matrix recovery with less number of parameters than the standard matrix completion algorithms.
Tasks	Denoising, Matrix Completion
Published	2019-11-26
URL	https://arxiv.org/abs/1911.11774v2
PDF	https://arxiv.org/pdf/1911.11774v2.pdf
PWC	https://paperswithcode.com/paper/stable-matrix-completion-using-properly
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Interpolation and Denoising of Seismic Data using Convolutional Neural Networks


Title	Interpolation and Denoising of Seismic Data using Convolutional Neural Networks
Authors	Sara Mandelli, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro
Abstract	Seismic data processing algorithms greatly benefit from regularly sampled and reliable data. Therefore, interpolation and denoising play a fundamental role as one of the starting steps of most seismic processing workflows. We exploit convolutional neural networks for the joint tasks of interpolation and random noise attenuation of 2D common shot gathers. Inspired by the great contributions achieved in image processing and computer vision, we investigate a particular architecture of convolutional neural network referred to as U-net, which implements a convolutional autoencoder able to describe the complex features of clean and regularly sampled data for reconstructing the corrupted ones. In training phase we exploit part of the data for tailoring the network to the specific tasks of interpolation, denoising and joint denoising/interpolation, while during the system deployment we are able to recover the remaining corrupted shot gathers in a computationally efficient procedure. We consider a plurality of data corruptions in our numerical experiments, including different noise models and different distributions of missing traces. Several examples on synthetic and field data illustrate the appealing features of the aforementioned strategy. Comparative examples show improvements with respect to recently proposed solutions for joint denoising and interpolation.
Tasks	Denoising
Published	2019-01-23
URL	https://arxiv.org/abs/1901.07927v4
PDF	https://arxiv.org/pdf/1901.07927v4.pdf
PWC	https://paperswithcode.com/paper/interpolation-and-denoising-of-seismic-data
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How implicit regularization of Neural Networks affects the learned function – Part I


Title	How implicit regularization of Neural Networks affects the learned function – Part I
Authors	Jakob Heiss, Josef Teichmann, Hanna Wutte
Abstract	Today, various forms of neural networks are trained to perform approximation tasks in many fields. However, the solutions obtained are not fully understood. Empirical results suggest that typical training algorithms favor regularized solutions. These observations motivate us to analyze properties of the solutions found by gradient descent initialized close to zero, that is frequently employed to perform the training task. As a starting point, we consider one dimensional (shallow) ReLU neural networks in which weights are chosen randomly and only the terminal layer is trained. We show that the resulting solution converges to the smooth spline interpolation of the training data as the number of hidden nodes tends to infinity. Moreover, we derive a correspondence between the early stopped gradient descent and the smoothing spline regression. This might give valuable insight on the properties of the solutions obtained using gradient descent methods in general settings.
Tasks
Published	2019-11-07
URL	https://arxiv.org/abs/1911.02903v2
PDF	https://arxiv.org/pdf/1911.02903v2.pdf
PWC	https://paperswithcode.com/paper/how-implicit-regularization-of-neural
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Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy


Title	Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy
Authors	Eleni Partalidou, Eleftherios Spyromitros-Xioufis, Stavros Doropoulos, Stavros Vologiannidis, Konstantinos I. Diamantaras
Abstract	This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into the source code, a feature that did not exist before our contribution, and was used for building the models. Additionally, a part of speech tagger was trained that can detect the morphology of the tokens and performs higher than the state-of-the-art results when classifying only the part of speech. For named entity recognition using spaCy, a model that extends the standard ENAMEX type (organization, location, person) was built. Certain experiments that were conducted indicate the need for flexibility in out-of-vocabulary words and there is an effort for resolving this issue. Finally, the evaluation results are discussed.
Tasks	Named Entity Recognition, Part-Of-Speech Tagging
Published	2019-12-05
URL	https://arxiv.org/abs/1912.10162v1
PDF	https://arxiv.org/pdf/1912.10162v1.pdf
PWC	https://paperswithcode.com/paper/design-and-implementation-of-an-open-source
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Adaptive Optimal Control for Reference Tracking Independent of Exo-System Dynamics


Title	Adaptive Optimal Control for Reference Tracking Independent of Exo-System Dynamics
Authors	Florian Köpf, Johannes Westermann, Michael Flad, Sören Hohmann
Abstract	Model-free control based on the idea of Reinforcement Learning is a promising approach that has recently gained extensive attention. However, Reinforcement-Learning-based control methods solely focus on the regulation problem or learn to track a reference that is generated by a time-invariant exo-system. In the latter case, controllers are only able to track the time-invariant reference dynamics which they have been trained on and need to be re-trained each time the reference dynamics change. Consequently, these methods fail in a number of applications which obviously rely on a trajectory not being generated by an exo-system. One prominent example is autonomous driving. This paper provides for the first time an adaptive optimal control method capable to track reference trajectories not being generated by a time-invariant exo-system. The main innovation is a novel Q-function that directly incorporates a given reference trajectory on a moving horizon. This new Q-function exhibits a particular structure which allows the design of an efficient, iterative, provably convergent Reinforcement Learning algorithm that enables optimal tracking. Two real-world examples demonstrate the effectiveness of our new method.
Tasks	Autonomous Driving
Published	2019-06-12
URL	https://arxiv.org/abs/1906.05085v5
PDF	https://arxiv.org/pdf/1906.05085v5.pdf
PWC	https://paperswithcode.com/paper/reinforcement-learning-based-adaptive-optimal
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Data collaboration analysis for distributed datasets


Title	Data collaboration analysis for distributed datasets
Authors	Akira Imakura, Tetsuya Sakurai
Abstract	In this paper, we propose a data collaboration analysis method for distributed datasets. The proposed method is a centralized machine learning while training datasets and models remain distributed over some institutions. Recently, data became large and distributed with decreasing costs of data collection. If we can centralize these distributed datasets and analyse them as one dataset, we expect to obtain novel insight and achieve a higher prediction performance compared with individual analyses on each distributed dataset. However, it is generally difficult to centralize the original datasets due to their huge data size or regarding a privacy-preserving problem. To avoid these difficulties, we propose a data collaboration analysis method for distributed datasets without sharing the original datasets. The proposed method centralizes only intermediate representation constructed individually instead of the original dataset.
Tasks
Published	2019-02-20
URL	http://arxiv.org/abs/1902.07535v1
PDF	http://arxiv.org/pdf/1902.07535v1.pdf
PWC	https://paperswithcode.com/paper/data-collaboration-analysis-for-distributed
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Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis


Title	Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis
Authors	Arun Verma, Manjesh K. Hanawal, Nandyala Hemachandra
Abstract	In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e.g., blood test, urine test, followed by invasive tests. As tests are often costly, one would like to obtain only those features (tests) that can establish the presence or absence of the state conclusively. Another aspect of medical diagnosis is that we are often faced with unsupervised prediction tasks as the true state of the patients may not be known. Motivated by such medical diagnosis problems, we consider a {\it Cost-Sensitive Medical Diagnosis} (CSMD) problem, where the true state of patients is unknown. We formulate the CSMD problem as a feature selection problem where each test gives a feature that can be used in a prediction model. Our objective is to learn strategies for selecting the features that give the best trade-off between accuracy and costs. We exploit the `Weak Dominance' property of problem to develop online algorithms that identify a set of features which provides an` optimal’ trade-off between cost and accuracy of prediction without requiring to know the true state of the medical condition. Our empirical results validate the performance of our algorithms on problem instances generated from real-world datasets.
Tasks	Feature Selection, Medical Diagnosis
Published	2019-12-25
URL	https://arxiv.org/abs/2001.00626v1
PDF	https://arxiv.org/pdf/2001.00626v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-online-feature-selection-for
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Radiomic Feature Stability Analysis based on Probabilistic Segmentations


Title	Radiomic Feature Stability Analysis based on Probabilistic Segmentations
Authors	Christoph Haarburger, Justus Schock, Daniel Truhn, Philippe Weitz, Gustav Mueller-Franzes, Leon Weninger, Dorit Merhof
Abstract	Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyze radiomics feature stability based on probabilistic segmentations. Based on a public lung cancer dataset, we generate an arbitrary number of plausible segmentations using a Probabilistic U-Net. From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations. Our results suggest that there are groups of radiomic features that are more (e.g. statistics features) and less (e.g. gray-level size zone matrix features) robust against segmentation variability. Finally, we demonstrate that segmentation variance impacts the performance of a prognostic lung cancer survival model and propose a new and potentially more robust radiomics feature selection workflow.
Tasks	Feature Selection
Published	2019-10-13
URL	https://arxiv.org/abs/1910.05693v2
PDF	https://arxiv.org/pdf/1910.05693v2.pdf
PWC	https://paperswithcode.com/paper/radiomic-feature-stability-analysis-based-on
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