January 28, 2020

2967 words 14 mins read

Paper Group ANR 1052

Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis. Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis. TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks. Automated Video Game Testing Using Synthetic and Human-Like Agents. Memory efficient brain tumor …

Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis


Title	Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis
Authors	Kosaku Takanashi, Kenichiro McAlinn
Abstract	This paper studies the theoretical predictive properties of classes of forecast combination methods. A novel strategy based on continuous time stochastic processes is proposed and developed, where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito’s lemma. We identify a class of forecast combination methods, which we categorize as non-linear synthesis, and find that it entails an extra term in the predictive error process that “corrects” the bias from misspecification and dependence amongst forecasts, effectively improving forecasts. We show that a subclass of the recently developed framework of Bayesian predictive synthesis fits within this class. Theoretical properties are examined and we show that non-linear synthesis improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions that are met in most real applications. We discuss the conditions for which non-linear synthesis outperforms linear combinations, and its implications for developing further strategies. A finite sample simulation study is presented to illustrate our results.
Tasks
Published	2019-11-20
URL	https://arxiv.org/abs/1911.08662v2
PDF	https://arxiv.org/pdf/1911.08662v2.pdf
PWC	https://paperswithcode.com/paper/predictive-properties-of-forecast-combination
Repo
Framework

Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis


Title	Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis
Authors	Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Maximilian Loeffler, Jan S. Kirschke, Bjoern H. Menze
Abstract	We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision. Building on this, we (i) design a loss function capable of modelling data variance on PCs which are unstructured, and (ii) regularise the latent space as in a variational auto-encoder, both of which increase the auto-encoders’ descriptive capacity while making them probabilistic. Evaluating the reconstruction quality of our architectures, we employ them for detecting vertebral fractures without any supervision. By learning to efficiently reconstruct only healthy vertebrae, fractures are detected as anomalous reconstructions. Evaluating on a dataset containing $\sim$1500 vertebrae, we achieve area-under-ROC curve of $>$75%, without using intensity-based features.
Tasks
Published	2019-07-22
URL	https://arxiv.org/abs/1907.09254v2
PDF	https://arxiv.org/pdf/1907.09254v2.pdf
PWC	https://paperswithcode.com/paper/probabilistic-point-cloud-reconstructions-for
Repo
Framework

TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks


Title	TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks
Authors	Minh H. Vu, Tufve Nyholm, Tommy Löfstedt
Abstract	Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set.
Tasks	Brain Tumor Segmentation, Semantic Segmentation
Published	2019-10-11
URL	https://arxiv.org/abs/1910.05338v3
PDF	https://arxiv.org/pdf/1910.05338v3.pdf
PWC	https://paperswithcode.com/paper/tunet-end-to-end-hierarchical-brain-tumor
Repo
Framework

Automated Video Game Testing Using Synthetic and Human-Like Agents


Title	Automated Video Game Testing Using Synthetic and Human-Like Agents
Authors	Sinan Ariyurek, Aysu Betin-Can, Elif Surer
Abstract	In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGPIRL captures multiple policies executed by human testers. These testers’ aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers’ bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance.
Tasks
Published	2019-06-02
URL	https://arxiv.org/abs/1906.00317v1
PDF	https://arxiv.org/pdf/1906.00317v1.pdf
PWC	https://paperswithcode.com/paper/190600317
Repo
Framework

Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net


Title	Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net
Authors	Markus Frey, Matthias Nau
Abstract	Early diagnosis and accurate segmentation of brain tumors are imperative for successful treatment. Unfortunately, manual segmentation is time consuming, costly and despite extensive human expertise often inaccurate. Here, we present an MRI-based tumor segmentation framework using an autoencoder-regularized 3D-convolutional neural network. We trained the model on manually segmented structural T1, T1ce, T2, and Flair MRI images of 335 patients with tumors of variable severity, size and location. We then tested the model using independent data of 125 patients and successfully segmented brain tumors into three subregions: the tumor core (TC), the enhancing tumor (ET) and the whole tumor (WT). We also explored several data augmentations and preprocessing steps to improve segmentation performance. Importantly, our model was implemented on a single NVIDIA GTX1060 graphics unit and hence optimizes tumor segmentation for widely affordable hardware. In sum, we present a memory-efficient and affordable solution to tumor segmentation to support the accurate diagnostics of oncological brain pathologies.
Tasks	Brain Tumor Segmentation
Published	2019-10-04
URL	https://arxiv.org/abs/1910.02058v1
PDF	https://arxiv.org/pdf/1910.02058v1.pdf
PWC	https://paperswithcode.com/paper/memory-efficient-brain-tumor-segmentation
Repo
Framework

Deep Reinforcement Learning for Detecting Malicious Websites


Title	Deep Reinforcement Learning for Detecting Malicious Websites
Authors	Moitrayee Chatterjee, Akbar Siami Namin
Abstract	Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A \textit{phisher} usually setups a deceptive website, where the victims are conned into entering credentials and sensitive information. It is therefore important to detect these types of malicious websites before causing any harmful damages to victims. Inspired by the evolving nature of the phishing websites, this paper introduces a novel approach based on deep reinforcement learning to model and detect malicious URLs. The proposed model is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.
Tasks
Published	2019-05-22
URL	https://arxiv.org/abs/1905.09207v1
PDF	https://arxiv.org/pdf/1905.09207v1.pdf
PWC	https://paperswithcode.com/paper/deep-reinforcement-learning-for-detecting
Repo
Framework

Prediction of Overall Survival of Brain Tumor Patients


Title	Prediction of Overall Survival of Brain Tumor Patients
Authors	Rupal Agravat, Mehul S Raval
Abstract	Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. In addition, features from the tumorous brain help in predicting patients overall survival. The main focus of this paper is to segment tumor from BRATS 2018 benchmark dataset and use age, shape and volumetric features to predict overall survival of patients. The random forest classifier achieves overall survival accuracy of 59% on the test dataset and 67% on the dataset with resection status as gross total resection. The proposed approach uses fewer features but achieves better accuracy than state of the art methods.
Tasks	Brain Tumor Segmentation
Published	2019-09-10
URL	https://arxiv.org/abs/1909.04596v1
PDF	https://arxiv.org/pdf/1909.04596v1.pdf
PWC	https://paperswithcode.com/paper/prediction-of-overall-survival-of-brain-tumor
Repo
Framework

An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder


Title	An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder
Authors	Xueying Tang, Zhi Wang, Jingchen Liu, Zhiliang Ying
Abstract	Computer simulations have become a popular tool of assessing complex skills such as problem-solving skills. Log files of computer-based items record the entire human-computer interactive processes for each respondent. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have been developed for exploiting the information contained in process data. In this article, we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computers interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.
Tasks
Published	2019-08-16
URL	https://arxiv.org/abs/1908.06075v1
PDF	https://arxiv.org/pdf/1908.06075v1.pdf
PWC	https://paperswithcode.com/paper/an-exploratory-analysis-of-the-latent
Repo
Framework

Subspace Match Probably Does Not Accurately Assess the Similarity of Learned Representations


Title	Subspace Match Probably Does Not Accurately Assess the Similarity of Learned Representations
Authors	Jeremiah Johnson
Abstract	Learning informative representations of data is one of the primary goals of deep learning, but there is still little understanding as to what representations a neural network actually learns. To better understand this, subspace match was recently proposed as a method for assessing the similarity of the representations learned by neural networks. It has been shown that two networks with the same architecture trained from different initializations learn representations that at hidden layers show low similarity when assessed with subspace match, even when the output layers show high similarity and the networks largely exhibit similar performance on classification tasks. In this note, we present a simple example motivated by standard results in commutative algebra to illustrate how this can happen, and show that although the subspace match at a hidden layer may be 0, the representations learned may be isomorphic as vector spaces. This leads us to conclude that a subspace match comparison of learned representations may well be uninformative, and it points to the need for better methods of understanding learned representations.
Tasks
Published	2019-01-03
URL	http://arxiv.org/abs/1901.00884v1
PDF	http://arxiv.org/pdf/1901.00884v1.pdf
PWC	https://paperswithcode.com/paper/subspace-match-probably-does-not-accurately
Repo
Framework

Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms


Title	Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms
Authors	Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn
Abstract	The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. Recently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducing windowed and non-windowed graph scattering transforms based upon a very general class of asymmetric wavelets. We show that these asymmetric graph scattering transforms have many of the same theoretical guarantees as their symmetric counterparts. This work helps bridge the gap between scattering and other graph neural networks by introducing a large family of networks with provable stability and invariance guarantees. This lays the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable theoretical properties.
Tasks
Published	2019-11-14
URL	https://arxiv.org/abs/1911.06253v1
PDF	https://arxiv.org/pdf/1911.06253v1.pdf
PWC	https://paperswithcode.com/paper/understanding-graph-neural-networks-with
Repo
Framework

Classi-Fly: Inferring Aircraft Categories from Open Data


Title	Classi-Fly: Inferring Aircraft Categories from Open Data
Authors	Martin Strohmeier, Matthew Smith, Vincent Lenders, Ivan Martinovic
Abstract	In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology, the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than two million aircraft. We show that we can obtain the correct aircraft category with an accuracy of over 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect sensitive aircraft such as police and surveillance aircraft using this method.
Tasks	Stock Market Prediction, Weather Forecasting
Published	2019-07-30
URL	https://arxiv.org/abs/1908.01061v1
PDF	https://arxiv.org/pdf/1908.01061v1.pdf
PWC	https://paperswithcode.com/paper/classi-fly-inferring-aircraft-categories-from
Repo
Framework

SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition


Title	SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition
Authors	Hongming Zhang, Hantian Ding, Yangqiu Song
Abstract	Selectional Preference (SP) is a commonly observed language phenomenon and proved to be useful in many natural language processing tasks. To provide a better evaluation method for SP models, we introduce SP-10K, a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. Three representative SP acquisition methods based on pseudo-disambiguation are evaluated with SP-10K. To demonstrate the importance of our dataset, we investigate the relationship between SP-10K and the commonsense knowledge in ConceptNet5 and show the potential of using SP to represent the commonsense knowledge. We also use the Winograd Schema Challenge to prove that the proposed new SP relations are essential for the hard pronoun coreference resolution problem.
Tasks	Coreference Resolution
Published	2019-05-14
URL	https://arxiv.org/abs/1906.02123v1
PDF	https://arxiv.org/pdf/1906.02123v1.pdf
PWC	https://paperswithcode.com/paper/190602123
Repo
Framework

A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections


Title	A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections
Authors	Cong Xu, Min Yang, Jin Zhang
Abstract	The implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work. In this paper, a series of subspace projections are constructed efficiently by using Household QR factorization. With the aid of these subspace projections, a fast deflation method, called SPCA-SP, is developed for SPCA. This method keeps a good tradeoff between various criteria, including sparsity, orthogonality, explained variance, balance of sparsity, and computational cost. Comparative experiments on the benchmark data sets confirm the effectiveness of the proposed method.
Tasks
Published	2019-12-03
URL	https://arxiv.org/abs/1912.01449v2
PDF	https://arxiv.org/pdf/1912.01449v2.pdf
PWC	https://paperswithcode.com/paper/a-fast-deflation-method-for-sparse-principal
Repo
Framework

Real or Fake? Learning to Discriminate Machine from Human Generated Text


Title	Real or Fake? Learning to Discriminate Machine from Human Generated Text
Authors	Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc’Aurelio Ranzato, Arthur Szlam
Abstract	Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficult. In part, this is because standard gradient-based methods are not readily applicable when the input is high-dimensional and discrete. Here, we side-step this issue by generating negatives using pre-trained auto-regressive language models. The EBM then works in the residual of the language model; and is trained to discriminate real text from text generated by the auto-regressive models. We investigate the generalization ability of residual EBMs, a pre-requisite for using them in other applications. We extensively analyze generalization for the task of classifying whether an input is machine or human generated, a natural task given the training loss and how we mine negatives. Overall, we observe that EBMs can generalize remarkably well to changes in the architecture of the generators producing negatives. However, EBMs exhibit more sensitivity to the training set used by such generators.
Tasks	Language Modelling, Text Generation
Published	2019-06-07
URL	https://arxiv.org/abs/1906.03351v2
PDF	https://arxiv.org/pdf/1906.03351v2.pdf
PWC	https://paperswithcode.com/paper/real-or-fake-learning-to-discriminate-machine
Repo
Framework

A Multi-Stage Clustering Framework for Automotive Radar Data


Title	A Multi-Stage Clustering Framework for Automotive Radar Data
Authors	Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
Abstract	Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a twostage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.
Tasks	Autonomous Driving
Published	2019-07-08
URL	https://arxiv.org/abs/1907.03511v1
PDF	https://arxiv.org/pdf/1907.03511v1.pdf
PWC	https://paperswithcode.com/paper/a-multi-stage-clustering-framework-for
Repo
Framework