January 28, 2020

3367 words 16 mins read

Paper Group ANR 1005

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field. Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information. A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation. Hiding Information in Big Data based on Deep Learning. Microsco …

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field


Title	QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field
Authors	Yicheng Chen, Angela Jakary, Sivakami Avadiappan, Christopher P. Hess, Janine M. Lupo
Abstract	Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology–brain tumor patients with radiation-induced cerebral microbleeds.
Tasks
Published	2019-05-08
URL	https://arxiv.org/abs/1905.03356v2
PDF	https://arxiv.org/pdf/1905.03356v2.pdf
PWC	https://paperswithcode.com/paper/190503356
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Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information


Title	Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information
Authors	Sarah Dean, Sarah Rich, Benjamin Recht
Abstract	Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-$N$ linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset.
Tasks	Recommendation Systems
Published	2019-12-20
URL	https://arxiv.org/abs/1912.10068v1
PDF	https://arxiv.org/pdf/1912.10068v1.pdf
PWC	https://paperswithcode.com/paper/recommendations-and-user-agency-the
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A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation


Title	A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
Authors	Qing Liu, Beiji Zou, Yang Zhao, Yixiong Liang
Abstract	Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-of-the-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connections among prediction branches and their learning capacity is limited. To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet. Specifically, deformable side-output unit and aggregation unit with deep supervisions are proposed to learn base functions and expansion coefficients in gradient boosting framework. By stacking aggregation units in a deep-to-shallow manner, models’ performances are gradually boosted along deep to shallow stages. BoostNet achieves superior results to existing deep OD and OC segmentation networks on the public dataset ORIGA.
Tasks
Published	2019-11-05
URL	https://arxiv.org/abs/1911.01648v1
PDF	https://arxiv.org/pdf/1911.01648v1.pdf
PWC	https://paperswithcode.com/paper/a-deep-gradient-boosting-network-for-optic
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Hiding Information in Big Data based on Deep Learning


Title	Hiding Information in Big Data based on Deep Learning
Authors	Dingju Zhu
Abstract	The current approach of information hiding based on deep learning model can not directly use the original data as carriers, which means the approach can not make use of the existing data in big data to hiding information. We proposed a novel method of information hiding in big data based on deep learning. Our method uses the existing data in big data as carriers and uses deep learning models to hide and extract secret messages in big data. The data amount of big data is unlimited and thus the data amount of secret messages hided in big data can also be unlimited. Before opponents want to extract secret messages from carriers, they need to find the carriers, however finding out the carriers from big data is just like finding out a box from the sea. Deep learning models are well known as deep black boxes in which the process from the input to the output is very complex, and thus the deep learning model for information hiding is almost impossible for opponents to reconstruct. The results also show that our method can hide secret messages safely, conveniently, quickly and with no limitation on the data amount.
Tasks
Published	2019-12-31
URL	https://arxiv.org/abs/1912.13156v2
PDF	https://arxiv.org/pdf/1912.13156v2.pdf
PWC	https://paperswithcode.com/paper/hiding-information-in-big-data-based-on-deep
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Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning


Title	Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
Authors	Giulio Bacchiani, Daniele Molinari, Marco Patander
Abstract	Expert human drivers perform actions relying on traffic laws and their previous experience. While traffic laws are easily embedded into an artificial brain, modeling human complex behaviors which come from past experience is a more challenging task. One of these behaviors is the capability of communicating intentions and negotiating the right of way through driving actions, as when a driver is entering a crowded roundabout and observes other cars movements to guess the best time to merge in. In addition, each driver has its own unique driving style, which is conditioned by both its personal characteristics, such as age and quality of sight, and external factors, such as being late or in a bad mood. For these reasons, the interaction between different drivers is not trivial to simulate in a realistic manner. In this paper, this problem is addressed by developing a microscopic simulator using a Deep Reinforcement Learning Algorithm based on a combination of visual frames, representing the perception around the vehicle, and a vector of numerical parameters. In particular, the algorithm called Asynchronous Advantage Actor-Critic has been extended to a multi-agent scenario in which every agent needs to learn to interact with other similar agents. Moreover, the model includes a novel architecture such that the driving style of each vehicle is adjustable by tuning some of its input parameters, permitting to simulate drivers with different levels of aggressiveness and desired cruising speeds.
Tasks
Published	2019-03-04
URL	http://arxiv.org/abs/1903.01365v1
PDF	http://arxiv.org/pdf/1903.01365v1.pdf
PWC	https://paperswithcode.com/paper/microscopic-traffic-simulation-by-cooperative
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Maximizing Mutual Information for Tacotron


Title	Maximizing Mutual Information for Tacotron
Authors	Peng Liu, Xixin Wu, Shiyin Kang, Guangzhi Li, Dan Su, Dong Yu
Abstract	End-to-end speech synthesis methods already achieve close-to-human quality performance. However compared to HMM-based and NN-based frame-to-frame regression methods, they are prone to some synthesis errors, such as missing or repeating words and incomplete synthesis. We attribute the comparatively high utterance error rate to the local information preference of conditional autoregressive models, and the ill-posed training objective of the model, which describes mostly the training status of the autoregressive module, but rarely that of the condition module. Inspired by InfoGAN, we propose to maximize the mutual information between the text condition and the predicted acoustic features to strengthen the dependency between them for CAR speech synthesis model, which would alleviate the local information preference issue and reduce the utterance error rate. The training objective of maximizing mutual information can be considered as a metric of the dependency between the autoregressive module and the condition module. Experiment results show that our method can reduce the utterance error rate.
Tasks	Speech Synthesis
Published	2019-08-30
URL	https://arxiv.org/abs/1909.01145v2
PDF	https://arxiv.org/pdf/1909.01145v2.pdf
PWC	https://paperswithcode.com/paper/maximizing-mutual-information-for-tacotron
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No-regret Exploration in Contextual Reinforcement Learning


Title	No-regret Exploration in Contextual Reinforcement Learning
Authors	Aditya Modi, Ambuj Tewari
Abstract	We consider the recently proposed reinforcement learning (RL) framework of Contextual Markov Decision Processes (CMDP), where the agent interacts with an adversarial sequence of episodic tabular MDPs. In addition, a context vector determining the MDP parameters is available to the agent at the start of each episode, thereby allowing it to learn a context-dependent near-optimal policy. In this paper, we propose a no-regret online RL algorithm in the setting where the MDP parameters are obtained from the context using generalized linear models (GLMs). We propose and analyze optimistic and randomized exploration methods which make (time and space) efficient online updates. The proposed framework subsumes/corrects previous work in this area and also improves previous known bounds in the special case where the contextual mapping is linear. In addition, we demonstrate a generic template to derive confidence sets using an online learning oracle and further give a lower bound analysis for the setting.
Tasks
Published	2019-03-14
URL	https://arxiv.org/abs/1903.06187v2
PDF	https://arxiv.org/pdf/1903.06187v2.pdf
PWC	https://paperswithcode.com/paper/contextual-markov-decision-processes-using
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A Method for Expressing and Displaying the Vehicle Behavior Distribution in Maintenance Work Zones


Title	A Method for Expressing and Displaying the Vehicle Behavior Distribution in Maintenance Work Zones
Authors	Qun Yang, Zhepu Xu, Saravanan Gurupackiam, Ping Wang
Abstract	Maintenance work zones on the road network have impacts on the normal travelling of vehicles, which increase the risk of traffic accidents. The traffic characteristic analysis in maintenance work zones is a basis for maintenance work zone related research such as layout design, traffic control and safety assessment. Due to the difficulty in vehicle microscopic behaviour data acquisition, traditional traffic characteristic analysis mainly focuses on macroscopic characteristics. With the development of data acquisition technology, it becomes much easier to obtain a large amount of microscopic behaviour data nowadays, which lays a good foundation for analysing the traffic characteristics from a new point of view. This paper puts forward a method for expressing and displaying the vehicle behaviour distribution in maintenance work zones. Using portable vehicle microscopic behaviour data acquisition devices, lots of data can be obtained. Based on this data, an endpoint detection technology is used to automatically extract the segments in behaviour data with violent fluctuations, which are segments where vehicles take behaviours such as acceleration or turning. Using the support vector machine classification method, the specific types of behaviours of the segments extracted can be identified, and together with a data combination method, a total of ten types of behaviours can be identified. Then the kernel density analysis is used to cluster different types of behaviours of all passing vehicles to show the distribution on maps. By this method, how vehicles travel through maintenance work zones, and how different vehicle behaviours distribute in maintenance work zones can be displayed intuitively on maps, which is a novel traffic characteristic and can shed light to maintenance work zone related researches such as safety assessment and design method.
Tasks
Published	2019-04-25
URL	http://arxiv.org/abs/1904.11786v1
PDF	http://arxiv.org/pdf/1904.11786v1.pdf
PWC	https://paperswithcode.com/paper/a-method-for-expressing-and-displaying-the
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Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data


Title	Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data
Authors	Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani
Abstract	In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed. Each pixel in the hyperspectral image considered as a node in this network. The nodes in the network are clustered using the fuzzy c-means clustering method. Diffusion least mean square strategy has been used to optimize the proposed cost function. To evaluate the proposed method, experiments are conducted on synthetic and real datasets. Simulation results based on spectral angle distance, abundance angle distance and reconstruction error metrics illustrate the advantage of the proposed algorithm compared with other methods.
Tasks
Published	2019-05-16
URL	https://arxiv.org/abs/1905.08032v1
PDF	https://arxiv.org/pdf/1905.08032v1.pdf
PWC	https://paperswithcode.com/paper/clustered-multitask-nonnegative-matrix
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Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling


Title	Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling
Authors	Mengying Zhu, Xiaolin Zheng, Yan Wang, Yuyuan Li, Qianqiao Liang
Abstract	As the cornerstone of modern portfolio theory, Markowitz’s mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation. In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users’ different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our proposed portfolio strategy has marked superiority across representative real-world market datasets in terms of extensive evaluation criteria.
Tasks	Decision Making
Published	2019-11-13
URL	https://arxiv.org/abs/1911.05309v2
PDF	https://arxiv.org/pdf/1911.05309v2.pdf
PWC	https://paperswithcode.com/paper/context-aware-dynamic-assets-selection-for
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Adaptive Bayesian Linear Regression for Automated Machine Learning


Title	Adaptive Bayesian Linear Regression for Automated Machine Learning
Authors	Weilin Zhou, Frederic Precioso
Abstract	To solve a machine learning problem, one typically needs to perform data preprocessing, modeling, and hyperparameter tuning, which is known as model selection and hyperparameter optimization.The goal of automated machine learning (AutoML) is to design methods that can automatically perform model selection and hyperparameter optimization without human interventions for a given dataset. In this paper, we propose a meta-learning method that can search for a high-performance machine learning pipeline from the predefined set of candidate pipelines for supervised classification datasets in an efficient way by leveraging meta-data collected from previous experiments. More specifically, our method combines an adaptive Bayesian regression model with a neural network basis function and the acquisition function from Bayesian optimization. The adaptive Bayesian regression model is able to capture knowledge from previous meta-data and thus make predictions of the performances of machine learning pipelines on a new dataset. The acquisition function is then used to guide the search of possible pipelines based on the predictions.The experiments demonstrate that our approach can quickly identify high-performance pipelines for a range of test datasets and outperforms the baseline methods.
Tasks	AutoML, Hyperparameter Optimization, Meta-Learning, Model Selection
Published	2019-04-01
URL	http://arxiv.org/abs/1904.00577v2
PDF	http://arxiv.org/pdf/1904.00577v2.pdf
PWC	https://paperswithcode.com/paper/adaptive-bayesian-linear-regression-for
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Cutting the Unnecessary Long Tail: Cost-Effective Big Data Clustering in the Cloud


Title	Cutting the Unnecessary Long Tail: Cost-Effective Big Data Clustering in the Cloud
Authors	Dongwei Li, Shuliang Wang, Nan Gao, Qiang He, Yun Yang
Abstract	Clustering big data often requires tremendous computational resources where cloud computing is undoubtedly one of the promising solutions. However, the computation cost in the cloud can be unexpectedly high if it cannot be managed properly. The long tail phenomenon has been observed widely in the big data clustering area, which indicates that the majority of time is often consumed in the middle to late stages in the clustering process. In this research, we try to cut the unnecessary long tail in the clustering process to achieve a sufficiently satisfactory accuracy at the lowest possible computation cost. A novel approach is proposed to achieve cost-effective big data clustering in the cloud. By training the regression model with the sampling data, we can make widely used k-means and EM (Expectation-Maximization) algorithms stop automatically at an early point when the desired accuracy is obtained. Experiments are conducted on four popular data sets and the results demonstrate that both k-means and EM algorithms can achieve high cost-effectiveness in the cloud with our proposed approach. For example, in the case studies with the much more efficient k-means algorithm, we find that achieving a 99% accuracy needs only 47.71%-71.14% of the computation cost required for achieving a 100% accuracy while the less efficient EM algorithm needs 16.69%-32.04% of the computation cost. To put that into perspective, in the United States land use classification example, our approach can save up to $94,687.49 for the government in each use.
Tasks
Published	2019-09-22
URL	https://arxiv.org/abs/1909.10000v1
PDF	https://arxiv.org/pdf/1909.10000v1.pdf
PWC	https://paperswithcode.com/paper/190910000
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PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms


Title	PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Authors	Wen Dong, Tong Guan, Bruno Lepri, Chunming Qiao
Abstract	Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases.
Tasks
Published	2019-05-07
URL	https://arxiv.org/abs/1905.02607v1
PDF	https://arxiv.org/pdf/1905.02607v1.pdf
PWC	https://paperswithcode.com/paper/pocketcare-tracking-the-flu-with-mobile
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Framework

Learning to Compose and Reason with Language Tree Structures for Visual Grounding


Title	Learning to Compose and Reason with Language Tree Structures for Visual Grounding
Authors	Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, Hanwang Zhang
Abstract	Grounding natural language in images, such as localizing “the black dog on the left of the tree”, is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained and compositional language space. However, existing solutions rely on the association between the holistic language features and visual features, while neglect the nature of compositional reasoning implied in the language. In this paper, we propose a natural language grounding model that can automatically compose a binary tree structure for parsing the language and then perform visual reasoning along the tree in a bottom-up fashion. We call our model RVG-TREE: Recursive Grounding Tree, which is inspired by the intuition that any language expression can be recursively decomposed into two constituent parts, and the grounding confidence score can be recursively accumulated by calculating their grounding scores returned by sub-trees. RVG-TREE can be trained end-to-end by using the Straight-Through Gumbel-Softmax estimator that allows the gradients from the continuous score functions passing through the discrete tree construction. Experiments on several benchmarks show that our model achieves the state-of-the-art performance with more explainable reasoning.
Tasks	Visual Reasoning
Published	2019-06-05
URL	https://arxiv.org/abs/1906.01784v1
PDF	https://arxiv.org/pdf/1906.01784v1.pdf
PWC	https://paperswithcode.com/paper/learning-to-compose-and-reason-with-language
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DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training


Title	DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training
Authors	Alessandro Rigazzi
Abstract	Data parallelism has become the de facto standard for training Deep Neural Network on multiple processing units. In this work we propose DC-S3GD, a decentralized (without Parameter Server) stale-synchronous version of the Delay-Compensated Asynchronous Stochastic Gradient Descent (DC-ASGD) algorithm. In our approach, we allow for the overlap of computation and communication, and compensate the inherent error with a first-order correction of the gradients. We prove the effectiveness of our approach by training Convolutional Neural Network with large batches and achieving state-of-the-art results.
Tasks
Published	2019-11-06
URL	https://arxiv.org/abs/1911.02516v1
PDF	https://arxiv.org/pdf/1911.02516v1.pdf
PWC	https://paperswithcode.com/paper/dc-s3gd-delay-compensated-stale-synchronous
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