April 2, 2020

3329 words 16 mins read

Paper Group ANR 108

A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series. Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization. Improving Label Ranking Ensembles using Boo …

A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series


Title	A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series
Authors	Yasuhiko Tachibana, Masataka Nishimori, Naoyuki Kitamura, Kensuke Umehara, Junko Ota, Takayuki Obata, Tatsuya Higashi
Abstract	Purpose: To construct a neural network model that can learn the different diagnosing strategies of radiologists to better classify aneurysm status in magnetic resonance angiography images. Materials and methods: This retrospective study included 3423 time-of-flight brain magnetic resonance angiography image series (subjects: male 1843 [mean age, 50.2 +/- 11.7 years], female 1580 [50.8 +/- 11.3 years]) recorded from November 2017 through January 2019. The image series were read independently for aneurysm status by one of four board-certified radiologists, who were assisted by an established deep learning-based computer-assisted diagnosis (CAD) system. The constructed neural networks were trained to classify the aneurysm status of zero to five aneurysm-suspicious areas suggested by the CAD system for each image series, and any additional aneurysm areas added by the radiologists, and this classification was compared with the judgment of the annotating radiologist. Image series were randomly allocated to training and testing data in an 8:2 ratio. The accuracy of the classification was compared by receiver operating characteristic analysis between the control model that accepted only image data as input and the proposed model that additionally accepted the information of who the annotating radiologist was. The DeLong test was used to compare areas under the curves (P < 0.05 was considered significant). Results: The area under the curve was larger in the proposed model (0.845) than in the control model (0.793), and the difference was significant (P < 0.0001). Conclusion: The proposed model improved classification accuracy by learning the diagnosis strategies of individual annotating radiologists.
Tasks
Published	2020-02-03
URL	https://arxiv.org/abs/2002.01891v1
PDF	https://arxiv.org/pdf/2002.01891v1.pdf
PWC	https://paperswithcode.com/paper/a-neural-network-model-that-learns
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Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization


Title	Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization
Authors	Shixiong Zhang, Xiangtao Li, Qiuzhen Lin, Ka-Chun Wong
Abstract	In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on two single-cell transcriptomic datasets.
Tasks	Dimensionality Reduction
Published	2020-01-03
URL	https://arxiv.org/abs/2001.01006v1
PDF	https://arxiv.org/pdf/2001.01006v1.pdf
PWC	https://paperswithcode.com/paper/review-of-single-cell-rna-seq-data-clustering
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Improving Label Ranking Ensembles using Boosting Techniques


Title	Improving Label Ranking Ensembles using Boosting Techniques
Authors	Lihi Dery, Erez Shmueli
Abstract	Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks. Extensive evaluation of the proposed algorithm on 24 semi-synthetic and real-world label ranking datasets shows that it significantly outperforms existing state-of-the-art label ranking algorithms.
Tasks
Published	2020-01-21
URL	https://arxiv.org/abs/2001.07744v1
PDF	https://arxiv.org/pdf/2001.07744v1.pdf
PWC	https://paperswithcode.com/paper/improving-label-ranking-ensembles-using
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Deep Neural Network Perception Models and Robust Autonomous Driving Systems


Title	Deep Neural Network Perception Models and Robust Autonomous Driving Systems
Authors	Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi, Paul Fieguth, Alexander Wong
Abstract	This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Tasks	Autonomous Driving
Published	2020-03-04
URL	https://arxiv.org/abs/2003.08756v1
PDF	https://arxiv.org/pdf/2003.08756v1.pdf
PWC	https://paperswithcode.com/paper/deep-neural-network-perception-models-and
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Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems


Title	Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
Authors	Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, Ping Li
Abstract	Neural networks of ads systems usually take input from multiple resources, e.g., query-ad relevance, ad features and user portraits. These inputs are encoded into one-hot or multi-hot binary features, with typically only a tiny fraction of nonzero feature values per example. Deep learning models in online advertising industries can have terabyte-scale parameters that do not fit in the GPU memory nor the CPU main memory on a computing node. For example, a sponsored online advertising system can contain more than $10^{11}$ sparse features, making the neural network a massive model with around 10 TB parameters. In this paper, we introduce a distributed GPU hierarchical parameter server for massive scale deep learning ads systems. We propose a hierarchical workflow that utilizes GPU High-Bandwidth Memory, CPU main memory and SSD as 3-layer hierarchical storage. All the neural network training computations are contained in GPUs. Extensive experiments on real-world data confirm the effectiveness and the scalability of the proposed system. A 4-node hierarchical GPU parameter server can train a model more than 2X faster than a 150-node in-memory distributed parameter server in an MPI cluster. In addition, the price-performance ratio of our proposed system is 4-9 times better than an MPI-cluster solution.
Tasks
Published	2020-03-12
URL	https://arxiv.org/abs/2003.05622v1
PDF	https://arxiv.org/pdf/2003.05622v1.pdf
PWC	https://paperswithcode.com/paper/distributed-hierarchical-gpu-parameter-server
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Optimal strategies in the Fighting Fantasy gaming system: influencing stochastic dynamics by gambling with limited resource


Title	Optimal strategies in the Fighting Fantasy gaming system: influencing stochastic dynamics by gambling with limited resource
Authors	Iain G. Johnston
Abstract	Fighting Fantasy is a popular recreational fantasy gaming system worldwide. Combat in this system progresses through a stochastic game involving a series of rounds, each of which may be won or lost. Each round, a limited resource (`luck’) may be spent on a gamble to amplify the benefit from a win or mitigate the deficit from a loss. However, the success of this gamble depends on the amount of remaining resource, and if the gamble is unsuccessful, benefits are reduced and deficits increased. Players thus dynamically choose to expend resource to attempt to influence the stochastic dynamics of the game, with diminishing probability of positive return. The identification of the optimal strategy for victory is a Markov decision problem that has not yet been solved. Here, we combine stochastic analysis and simulation with dynamic programming to characterise the dynamical behaviour of the system in the absence and presence of gambling policy. We derive a simple expression for the victory probability without luck-based strategy. We use a backward induction approach to solve the Bellman equation for the system and identify the optimal strategy for any given state during the game. The optimal control strategies can dramatically enhance success probabilities, but take detailed forms; we use stochastic simulation to approximate these optimal strategies with simple heuristics that can be practically employed. Our findings provide a roadmap to improving success in the games that millions of people play worldwide, and inform a class of resource allocation problems with diminishing returns in stochastic games. \|
Tasks
Published	2020-02-24
URL	https://arxiv.org/abs/2002.10172v1
PDF	https://arxiv.org/pdf/2002.10172v1.pdf
PWC	https://paperswithcode.com/paper/optimal-strategies-in-the-fighting-fantasy
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The Gâteaux-Hopfield Neural Network method


Title	The Gâteaux-Hopfield Neural Network method
Authors	Felipe Silva Carvalho, João Pedro Braga
Abstract	In the present work a new set of differential equations for the Hopfield Neural Network (HNN) method were established by means of the Linear Extended Gateaux Derivative (LEGD). This new approach will be referred to as G^ateaux-Hopfiel Neural Network (GHNN). A first order Fredholm integral problem was used to test this new method and it was found to converge 22 times faster to the exact solutions for {\alpha} > 1 if compared with the HNN integer order differential equations. Also a limit to the learning time is observed by analysing the results for different values of {\alpha}. The robustness and advantages of this new method will be pointed out.
Tasks
Published	2020-01-29
URL	https://arxiv.org/abs/2001.11853v1
PDF	https://arxiv.org/pdf/2001.11853v1.pdf
PWC	https://paperswithcode.com/paper/the-gateaux-hopfield-neural-network-method
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Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks


Title	Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks
Authors	Weimin Zhou, Mark A. Anastasio
Abstract	The Ideal Observer (IO) performance has been advocated when optimizing medical imaging systems for signal detection tasks. However, analytical computation of the IO test statistic is generally intractable. To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed. However, current applications of MCMC techniques have been limited to several object models such as a lumpy object model and a binary texture model, and it remains unclear how MCMC methods can be implemented with other more sophisticated object models. Deep learning methods that employ generative adversarial networks (GANs) hold great promise to learn stochastic object models (SOMs) from image data. In this study, we described a method to approximate the IO by applying MCMC techniques to SOMs learned by use of GANs. The proposed method can be employed with arbitrary object models that can be learned by use of GANs, thereby the domain of applicability of MCMC techniques for approximating the IO performance is extended. In this study, both signal-known-exactly (SKE) and signal-known-statistically (SKS) binary signal detection tasks are considered. The IO performance computed by the proposed method is compared to that computed by the conventional MCMC method. The advantages of the proposed method are discussed.
Tasks
Published	2020-01-26
URL	https://arxiv.org/abs/2001.09526v1
PDF	https://arxiv.org/pdf/2001.09526v1.pdf
PWC	https://paperswithcode.com/paper/markov-chain-monte-carlo-approximation-of-the
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Generalized Neural Policies for Relational MDPs


Title	Generalized Neural Policies for Relational MDPs
Authors	Sankalp Garg, Aniket Bajpai, Mausam
Abstract	A Relational Markov Decision Process (RMDP) is a first-order representation to express all instances of a single probabilistic planning domain with possibly unbounded number of objects. Early work in RMDPs outputs generalized (instance-independent) first-order policies or value functions as a means to solve all instances of a domain at once. Unfortunately, this line of work met with limited success due to inherent limitations of the representation space used in such policies or value functions. Can neural models provide the missing link by easily representing more complex generalized policies, thus making them effective on all instances of a given domain? We present the first neural approach for solving RMDPs, expressed in the probabilistic planning language of RDDL. Our solution first converts an RDDL instance into a ground DBN. We then extract a graph structure from the DBN. We train a relational neural model that computes an embedding for each node in the graph and also scores each ground action as a function over the first-order action variable and object embeddings on which the action is applied. In essence, this represents a neural generalized policy for the whole domain. Given a new test problem of the same domain, we can compute all node embeddings using trained parameters and score each ground action to choose the best action using a single forward pass without any retraining. Our experiments on nine RDDL domains from IPPC demonstrate that neural generalized policies are significantly better than random and sometimes even more effective than training a state-of-the-art deep reactive policy from scratch.
Tasks
Published	2020-02-18
URL	https://arxiv.org/abs/2002.07375v1
PDF	https://arxiv.org/pdf/2002.07375v1.pdf
PWC	https://paperswithcode.com/paper/generalized-neural-policies-for-relational
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Super-Resolving Commercial Satellite Imagery Using Realistic Training Data


Title	Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
Authors	Xiang Zhu, Hossein Talebi, Xinwei Shi, Feng Yang, Peyman Milanfar
Abstract	In machine learning based single image super-resolution, the degradation model is embedded in training data generation. However, most existing satellite image super-resolution methods use a simple down-sampling model with a fixed kernel to create training images. These methods work fine on synthetic data, but do not perform well on real satellite images. We propose a realistic training data generation model for commercial satellite imagery products, which includes not only the imaging process on satellites but also the post-process on the ground. We also propose a convolutional neural network optimized for satellite images. Experiments show that the proposed training data generation model is able to improve super-resolution performance on real satellite images.
Tasks	Image Super-Resolution, Super-Resolution
Published	2020-02-26
URL	https://arxiv.org/abs/2002.11248v1
PDF	https://arxiv.org/pdf/2002.11248v1.pdf
PWC	https://paperswithcode.com/paper/super-resolving-commercial-satellite-imagery
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A Two-step-training Deep Learning Framework for Real-time Computational Imaging without Physics Priors


Title	A Two-step-training Deep Learning Framework for Real-time Computational Imaging without Physics Priors
Authors	Ruibo Shang, Kevin Hoffer-Hawlik, Geoffrey P. Luke
Abstract	Deep learning (DL) is a powerful tool in computational imaging for many applications. A common strategy is to reconstruct a preliminary image as the input of a neural network to achieve an optimized image. Usually, the preliminary image is acquired with the prior knowledge of the model. One outstanding challenge, however, is that the model is sometimes difficult to acquire with high accuracy. In this work, a two-step-training DL (TST-DL) framework is proposed for real-time computational imaging without prior knowledge of the model. A single fully-connected layer (FCL) is trained to directly learn the model with the raw measurement data as input and the image as output. Then, this pre-trained FCL is fixed and connected with an un-trained deep convolutional network for a second-step training to improve the output image fidelity. This approach has three main advantages. First, no prior knowledge of the model is required since the first-step training is to directly learn the model. Second, real-time imaging can be achieved since the raw measurement data is directly used as the input to the model. Third, it can handle any dimension of the network input and solve the input-output dimension mismatch issues which arise in convolutional neural networks. We demonstrate this framework in the applications of single-pixel imaging and photoacoustic imaging for linear model cases. The results are quantitatively compared with those from other DL frameworks and model-based optimization approaches. Noise robustness and the required size of the training dataset are studied for this framework. We further extend this concept to nonlinear models in the application of image de-autocorrelation by using multiple FCLs in the first-step training. Overall, this TST-DL framework is widely applicable to many computational imaging techniques for real-time image reconstruction without the physics priors.
Tasks	Image Reconstruction
Published	2020-01-10
URL	https://arxiv.org/abs/2001.03493v1
PDF	https://arxiv.org/pdf/2001.03493v1.pdf
PWC	https://paperswithcode.com/paper/a-two-step-training-deep-learning-framework
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Framework

Improving Face Recognition from Hard Samples via Distribution Distillation Loss


Title	Improving Face Recognition from Hard Samples via Distribution Distillation Loss
Authors	Yuge Huang, Pengcheng Shen, Ying Tai, Shaoxin Li, Xiaoming Liu, Jilin Li, Feiyue Huang, Rongrong Ji
Abstract	Large facial variations are the main challenge in face recognition. To this end, previous variation-specific methods make full use of task-related prior to design special network losses, which are typically not general among different tasks and scenarios. In contrast, the existing generic methods focus on improving the feature discriminability to minimize the intra-class distance while maximizing the interclass distance, which perform well on easy samples but fail on hard samples. To improve the performance on those hard samples for general tasks, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations. Specifically, we first adopt state-of-the-art classifiers such as ArcFace to construct two similarity distributions: teacher distribution from easy samples and student distribution from hard samples. Then, we propose a novel distribution-driven loss to constrain the student distribution to approximate the teacher distribution, which thus leads to smaller overlap between the positive and negative pairs in the student distribution. We have conducted extensive experiments on both generic large-scale face benchmarks and benchmarks with diverse variations on race, resolution and pose. The quantitative results demonstrate the superiority of our method over strong baselines, e.g., Arcface and Cosface.
Tasks	Face Recognition
Published	2020-02-10
URL	https://arxiv.org/abs/2002.03662v2
PDF	https://arxiv.org/pdf/2002.03662v2.pdf
PWC	https://paperswithcode.com/paper/distribution-distillation-loss-generic
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Improving Language Identification for Multilingual Speakers


Title	Improving Language Identification for Multilingual Speakers
Authors	Andrew Titus, Jan Silovsky, Nanxin Chen, Roger Hsiao, Mary Young, Arnab Ghoshal
Abstract	Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language. One aspect that has been mostly neglected, however, is discrimination of languages for multilingual speakers, despite being a primary target audience of many systems that utilize LID technologies. As we show in this work, LID systems can have a high average accuracy for most combinations of languages while greatly underperforming for others when accented speech is present. We address this by using coarser-grained targets for the acoustic LID model and integrating its outputs with interaction context signals in a context-aware model to tailor the system to each user. This combined system achieves an average 97% accuracy across all language combinations while improving worst-case accuracy by over 60% relative to our baseline.
Tasks	Language Identification
Published	2020-01-29
URL	https://arxiv.org/abs/2001.11019v1
PDF	https://arxiv.org/pdf/2001.11019v1.pdf
PWC	https://paperswithcode.com/paper/improving-language-identification-for
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Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving


Title	Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving
Authors	Na Du, Feng Zhou, Elizabeth Pulver, Dawn M. Tilbury, Lionel P. Robert, Anuj K. Pradhan, X. Jessie Yang
Abstract	In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.
Tasks
Published	2020-01-13
URL	https://arxiv.org/abs/2001.04509v1
PDF	https://arxiv.org/pdf/2001.04509v1.pdf
PWC	https://paperswithcode.com/paper/examining-the-effects-of-emotional-valence
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Framework

Cross-Lingual Adaptation Using Universal Dependencies


Title	Cross-Lingual Adaptation Using Universal Dependencies
Authors	Nasrin Taghizadeh, Heshaam Faili
Abstract	We describe a cross-lingual adaptation method based on syntactic parse trees obtained from the Universal Dependencies (UD), which are consistent across languages, to develop classifiers in low-resource languages. The idea of UD parsing is to capture similarities as well as idiosyncrasies among typologically different languages. In this paper, we show that models trained using UD parse trees for complex NLP tasks can characterize very different languages. We study two tasks of paraphrase identification and semantic relation extraction as case studies. Based on UD parse trees, we develop several models using tree kernels and show that these models trained on the English dataset can correctly classify data of other languages e.g. French, Farsi, and Arabic. The proposed approach opens up avenues for exploiting UD parsing in solving similar cross-lingual tasks, which is very useful for languages that no labeled data is available for them.
Tasks	Paraphrase Identification, Relation Extraction
Published	2020-03-24
URL	https://arxiv.org/abs/2003.10816v2
PDF	https://arxiv.org/pdf/2003.10816v2.pdf
PWC	https://paperswithcode.com/paper/cross-lingual-adaptation-using-universal
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