Paper Group ANR 899
A Method to Generate Synthetically Warped Document Image. Assigning Medical Codes at the Encounter Level by Paying Attention to Documents. Imputation estimators for unnormalized models with missing data. Decentralized Federated Learning: A Segmented Gossip Approach. Automating Predictive Modeling Process using Reinforcement Learning. Toward a bette …
A Method to Generate Synthetically Warped Document Image
| Title | A Method to Generate Synthetically Warped Document Image |
| Authors | Arpan Garai, Samit Biswas, Sekhar Mandal, Bidyut. B. Chaudhuri |
| Abstract | The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way. |
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| Published | 2019-10-15 |
| URL | https://arxiv.org/abs/1910.06621v1 |
| https://arxiv.org/pdf/1910.06621v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-method-to-generate-synthetically-warped |
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Assigning Medical Codes at the Encounter Level by Paying Attention to Documents
| Title | Assigning Medical Codes at the Encounter Level by Paying Attention to Documents |
| Authors | Han-Chin Shing, Guoli Wang, Philip Resnik |
| Abstract | The vast majority of research in computer assisted medical coding focuses on coding at the document level, but a substantial proportion of medical coding in the real world involves coding at the level of clinical encounters, each of which is typically represented by a potentially large set of documents. We introduce encounter-level document attention networks, which use hierarchical attention to explicitly take the hierarchical structure of encounter documentation into account. Experimental evaluation demonstrates improvements in coding accuracy as well as facilitation of human reviewers in their ability to identify which documents within an encounter play a role in determining the encounter level codes. |
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| Published | 2019-11-15 |
| URL | https://arxiv.org/abs/1911.06848v1 |
| https://arxiv.org/pdf/1911.06848v1.pdf | |
| PWC | https://paperswithcode.com/paper/assigning-medical-codes-at-the-encounter |
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Imputation estimators for unnormalized models with missing data
| Title | Imputation estimators for unnormalized models with missing data |
| Authors | Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim |
| Abstract | We propose estimation methods for unnormalized models with missing data. The key concept is to combine a modern imputation technique with estimators for unnormalized models including noise contrastive estimation and score matching. Further, we derive asymptotic distributions of the proposed estimators and construct the confidence intervals. The application to truncated Gaussian graphical models with missing data shows the validity of the proposed methods. |
| Tasks | Imputation |
| Published | 2019-03-08 |
| URL | http://arxiv.org/abs/1903.03630v1 |
| http://arxiv.org/pdf/1903.03630v1.pdf | |
| PWC | https://paperswithcode.com/paper/imputation-estimators-for-unnormalized-models |
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Decentralized Federated Learning: A Segmented Gossip Approach
| Title | Decentralized Federated Learning: A Segmented Gossip Approach |
| Authors | Chenghao Hu, Jingyan Jiang, Zhi Wang |
| Abstract | The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized topologies and the assumption of large nodes-to-server bandwidths. However, in real-world federated learning scenarios the network capacities between nodes are highly uniformly distributed and smaller than that in a datacenter. It is of great challenges for conventional federated learning approaches to efficiently utilize network capacities between nodes. In this paper, we propose a model segment level decentralized federated learning to tackle this problem. In particular, we propose a segmented gossip approach, which not only makes full utilization of node-to-node bandwidth, but also has good training convergence. The experimental results show that even the training time can be highly reduced as compared to centralized federated learning. |
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| Published | 2019-08-21 |
| URL | https://arxiv.org/abs/1908.07782v1 |
| https://arxiv.org/pdf/1908.07782v1.pdf | |
| PWC | https://paperswithcode.com/paper/190807782 |
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Automating Predictive Modeling Process using Reinforcement Learning
| Title | Automating Predictive Modeling Process using Reinforcement Learning |
| Authors | Udayan Khurana, Horst Samulowitz |
| Abstract | Building a good predictive model requires an array of activities such as data imputation, feature transformations, estimator selection, hyper-parameter search and ensemble construction. Given the large, complex and heterogenous space of options, off-the-shelf optimization methods are infeasible for realistic response times. In practice, much of the predictive modeling process is conducted by experienced data scientists, who selectively make use of available tools. Over time, they develop an understanding of the behavior of operators, and perform serial decision making under uncertainty, colloquially referred to as educated guesswork. With an unprecedented demand for application of supervised machine learning, there is a call for solutions that automatically search for a good combination of parameters across these tasks to minimize the modeling error. We introduce a novel system called APRL (Autonomous Predictive modeler via Reinforcement Learning), that uses past experience through reinforcement learning to optimize such sequential decision making from within a set of diverse actions under a time constraint on a previously unseen predictive learning problem. APRL actions are taken to optimize the performance of a final ensemble. This is in contrast to other systems, which maximize individual model accuracy first and create ensembles as a disconnected post-processing step. As a result, APRL is able to reduce up to 71% of classification error on average over a wide variety of problems. |
| Tasks | Decision Making, Decision Making Under Uncertainty, Imputation |
| Published | 2019-03-02 |
| URL | http://arxiv.org/abs/1903.00743v1 |
| http://arxiv.org/pdf/1903.00743v1.pdf | |
| PWC | https://paperswithcode.com/paper/automating-predictive-modeling-process-using |
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Toward a better trade-off between performance and fairness with kernel-based distribution matching
| Title | Toward a better trade-off between performance and fairness with kernel-based distribution matching |
| Authors | Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel |
| Abstract | As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences. How should we address this problem in a real-world system? How should we balance core performance and fairness metrics? In this paper, we introduce a MinDiff framework for regularizing classifiers toward different fairness metrics and analyze a technique with kernel-based statistical dependency tests. We run a thorough study on an academic dataset to compare the Pareto frontier achieved by different regularization approaches, and apply our kernel-based method to two large-scale industrial systems demonstrating real-world improvements. |
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| Published | 2019-10-25 |
| URL | https://arxiv.org/abs/1910.11779v1 |
| https://arxiv.org/pdf/1910.11779v1.pdf | |
| PWC | https://paperswithcode.com/paper/toward-a-better-trade-off-between-performance |
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VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
| Title | VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository |
| Authors | Kevin Hu, Neil Gaikwad, Michiel Bakker, Madelon Hulsebos, Emanuel Zgraggen, César Hidalgo, Tim Kraska, Guoliang Li, Arvind Satyanarayan, Çağatay Demiralp |
| Abstract | Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs. These exemplars often lack the characteristics of real-world datasets, and their one-off nature makes it difficult to compare different techniques. In this paper, we present VizNet: a large-scale corpus of over 31 million datasets compiled from open data repositories and online visualization galleries. On average, these datasets comprise 17 records over 3 dimensions and across the corpus, we find 51% of the dimensions record categorical data, 44% quantitative, and only 5% temporal. VizNet provides the necessary common baseline for comparing visualization design techniques, and developing benchmark models and algorithms for automating visual analysis. To demonstrate VizNet’s utility as a platform for conducting online crowdsourced experiments at scale, we replicate a prior study assessing the influence of user task and data distribution on visual encoding effectiveness, and extend it by considering an additional task: outlier detection. To contend with running such studies at scale, we demonstrate how a metric of perceptual effectiveness can be learned from experimental results, and show its predictive power across test datasets. |
| Tasks | Outlier Detection |
| Published | 2019-05-12 |
| URL | https://arxiv.org/abs/1905.04616v1 |
| https://arxiv.org/pdf/1905.04616v1.pdf | |
| PWC | https://paperswithcode.com/paper/viznet-towards-a-large-scale-visualization |
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Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models
| Title | Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models |
| Authors | Balasubramanya T. Nadiga, Chiyu Jiang, Daniel Livescu |
| Abstract | We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or neglected dynamics on resolved scales. We start by considering various methods of calibrating and analyzing such a model given a few well-resolved simulations. After presenting results for various point estimates and discussing some of their shortcomings, we demonstrate (a) the potential of hierarchical Bayesian analysis to uncover previously unanticipated physical dependencies in the approximate model, and (b) how such insights can then be used to improve the model. In effect parametric dependencies found from the Bayesian analysis are used to improve structural aspects of the model. While we choose to illustrate the procedure in the context of a closure model for buoyancy-driven, variable-density turbulence, the statistical nature of the approach makes it more generally applicable. Towards addressing issues of increased computational cost associated with the procedure, we demonstrate the use of a neural network based surrogate in accelerating the posterior sampling process and point to recent developments in variational inference as an alternative methodology for greatly mitigating such costs. We conclude by suggesting that modern validation and uncertainty quantification techniques such as the ones we consider have a valuable role to play in the development and improvement of approximate models. |
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| Published | 2019-05-20 |
| URL | https://arxiv.org/abs/1905.08227v1 |
| https://arxiv.org/pdf/1905.08227v1.pdf | |
| PWC | https://paperswithcode.com/paper/leveraging-bayesian-analysis-to-improve |
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Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation
| Title | Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation |
| Authors | Jingyuan Wang, Ning Wu, Wayne Xin Zhao, Fanzhang Peng, Xin Lin |
| Abstract | Personalized Route Recommendation (PRR) aims to generate user-specific route suggestions in response to users’ route queries. Early studies cast the PRR task as a pathfinding problem on graphs, and adopt adapted search algorithms by integrating heuristic strategies. Although these methods are effective to some extent, they require setting the cost functions with heuristics. In addition, it is difficult to utilize useful context information in the search procedure. To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task. Our model consists of two components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use a value network for estimating the cost from a candidate location to the destination. For capturing structural characteristics, the value network is built on top of improved graph attention networks by incorporating the moving state of a user and other context information. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model. |
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| Published | 2019-07-19 |
| URL | https://arxiv.org/abs/1907.08489v1 |
| https://arxiv.org/pdf/1907.08489v1.pdf | |
| PWC | https://paperswithcode.com/paper/empowering-a-search-algorithms-with-neural |
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A Short Survey On Memory Based Reinforcement Learning
| Title | A Short Survey On Memory Based Reinforcement Learning |
| Authors | Dhruv Ramani |
| Abstract | Reinforcement learning (RL) is a branch of machine learning which is employed to solve various sequential decision making problems without proper supervision. Due to the recent advancement of deep learning, the newly proposed Deep-RL algorithms have been able to perform extremely well in sophisticated high-dimensional environments. However, even after successes in many domains, one of the major challenge in these approaches is the high magnitude of interactions with the environment required for efficient decision making. Seeking inspiration from the brain, this problem can be solved by incorporating instance based learning by biasing the decision making on the memories of high rewarding experiences. This paper reviews various recent reinforcement learning methods which incorporate external memory to solve decision making and a survey of them is presented. We provide an overview of the different methods - along with their advantages and disadvantages, applications and the standard experimentation settings used for memory based models. This review hopes to be a helpful resource to provide key insight of the recent advances in the field and provide help in further future development of it. |
| Tasks | Decision Making |
| Published | 2019-04-14 |
| URL | http://arxiv.org/abs/1904.06736v1 |
| http://arxiv.org/pdf/1904.06736v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-short-survey-on-memory-based-reinforcement |
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Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
| Title | Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles |
| Authors | Dror Simon, Miriam Farber, Roman Goldenberg |
| Abstract | Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data. Wrong or inaccurate annotations generated by the ensemble may lead to performance degradation of the trained model. To deal with this problem we propose filtering the auto-labeled data using a trained model that predicts the quality of the annotation from the degree of consensus between ensemble models. Using semantic segmentation as an example, we show the advantage of the proposed auto-annotation filtering over training on data contaminated with inaccurate labels. Moreover, our experimental results show that in the case of semantic segmentation, the performance of a state-of-the-art model can be achieved by training it with only a fraction (30$%$) of the original manually labeled data set, and replacing the rest with the auto-annotated, quality filtered labels. |
| Tasks | Semantic Segmentation |
| Published | 2019-10-30 |
| URL | https://arxiv.org/abs/1910.13988v1 |
| https://arxiv.org/pdf/1910.13988v1.pdf | |
| PWC | https://paperswithcode.com/paper/auto-annotation-quality-prediction-for-semi |
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Decaying momentum helps neural network training
| Title | Decaying momentum helps neural network training |
| Authors | John Chen, Anastasios Kyrillidis |
| Abstract | Momentum is a simple and popular technique in deep learning for gradient-based optimizers. We propose a decaying momentum (Demon) rule, motivated by decaying the total contribution of a gradient to all future updates. Applying Demon to Adam leads to significantly improved training, notably competitive to momentum SGD with learning rate decay, even in settings in which adaptive methods are typically non-competitive. Similarly, applying Demon to momentum SGD rivals momentum SGD with learning rate decay, and in many cases leads to improved performance. Demon is trivial to implement and incurs limited extra computational overhead, compared to the vanilla counterparts. |
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| Published | 2019-10-11 |
| URL | https://arxiv.org/abs/1910.04952v1 |
| https://arxiv.org/pdf/1910.04952v1.pdf | |
| PWC | https://paperswithcode.com/paper/decaying-momentum-helps-neural-network |
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Cameras Viewing Cameras Geometry
| Title | Cameras Viewing Cameras Geometry |
| Authors | Danail Brezov, Michael Werman |
| Abstract | A basic problem in computer vision is to understand the structure of a real-world scene given several images of it. Here we study several theoretical aspects of the intra multi-view geometry of calibrated cameras when all that they can reliably recognize is each other. With the proliferation of wearable cameras, autonomous vehicles and drones, the geometry of these multiple cameras is a timely and relevant problem to study. |
| Tasks | Autonomous Vehicles |
| Published | 2019-11-28 |
| URL | https://arxiv.org/abs/1911.12706v1 |
| https://arxiv.org/pdf/1911.12706v1.pdf | |
| PWC | https://paperswithcode.com/paper/cameras-viewing-cameras-geometry |
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Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model
| Title | Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model |
| Authors | Hamid Mohammadi, Seyed Hossein Khasteh |
| Abstract | Evaluating the readability of a text can significantly facilitate the precise expression of information in a written form. The formulation of text readability assessment demands the identification of meaningful properties of the text and correct conversion of features to the right readability level. Sophisticated features and models are being used to evaluate the comprehensibility of texts accurately. Still, these models are challenging to implement, heavily language-dependent, and do not perform well on short texts. Deep reinforcement learning models are demonstrated to be helpful in further improvement of state-of-the-art text readability assessment models. The main contributions of the proposed approach are the automation of feature extraction, loosening the tight language dependency of text readability assessment task, and efficient use of text by finding the minimum portion of a text required to assess its readability. The experiments on Weebit, Cambridge Exams, and Persian readability datasets display the model’s state-of-the-art precision, efficiency, and the capability to be applied to other languages. |
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| Published | 2019-12-12 |
| URL | https://arxiv.org/abs/1912.05957v2 |
| https://arxiv.org/pdf/1912.05957v2.pdf | |
| PWC | https://paperswithcode.com/paper/text-as-environment-a-deep-reinforcement |
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Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
| Title | Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges |
| Authors | Lei Lei, Yue Tan, Shiwen Liu, Kuan Zhuang, Kan Zheng, Xuemin Shen |
| Abstract | The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect the status of physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The sensors collect information on the system status, based on which intelligent agents in IoT devices as well as Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide comprehensive survey of the state-of-art research, and then propose a general model for the applications of RL/DRL in AIoT. Finally, the challenges and open issues for future research are identified. |
| Tasks | Decision Making |
| Published | 2019-07-22 |
| URL | https://arxiv.org/abs/1907.09059v2 |
| https://arxiv.org/pdf/1907.09059v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-reinforcement-learning-for-autonomous-1 |
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