Paper Group ANR 256
Hierarchical Macro Strategy Model for MOBA Game AI. Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation. GEN Model: An Alternative Approach to Deep Neural Network Models. Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection. A Method Based on C …
Hierarchical Macro Strategy Model for MOBA Game AI
| Title | Hierarchical Macro Strategy Model for MOBA Game AI |
| Authors | Bin Wu, Qiang Fu, Jing Liang, Peng Qu, Xiaoqian Li, Liang Wang, Wei Liu, Wei Yang, Yongsheng Liu |
| Abstract | The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated cross-agent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system. |
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| Published | 2018-12-19 |
| URL | http://arxiv.org/abs/1812.07887v1 |
| http://arxiv.org/pdf/1812.07887v1.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-macro-strategy-model-for-moba |
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Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
| Title | Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation |
| Authors | Weihao Yuan, Kaiyu Hang, Haoran Song, Danica Kragic, Michael Y. Wang, Johannes A. Stork |
| Abstract | Moving a human body or a large and bulky object can require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot’s arms and relies on global properties of the interaction to succeed—rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting. |
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| Published | 2018-09-12 |
| URL | http://arxiv.org/abs/1809.04322v1 |
| http://arxiv.org/pdf/1809.04322v1.pdf | |
| PWC | https://paperswithcode.com/paper/reinforcement-learning-in-topology-based |
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GEN Model: An Alternative Approach to Deep Neural Network Models
| Title | GEN Model: An Alternative Approach to Deep Neural Network Models |
| Authors | Jiawei Zhang, Limeng Cui, Fisher B. Gouza |
| Abstract | In this paper, we introduce an alternative approach, namely GEN (Genetic Evolution Network) Model, to the deep learning models. Instead of building one single deep model, GEN adopts a genetic-evolutionary learning strategy to build a group of unit models generations by generations. Significantly different from the wellknown representation learning models with extremely deep structures, the unit models covered in GEN are of a much shallower architecture. In the training process, from each generation, a subset of unit models will be selected based on their performance to evolve and generate the child models in the next generation. GEN has significant advantages compared with existing deep representation learning models in terms of both learning effectiveness, efficiency and interpretability of the learning process and learned results. Extensive experiments have been done on diverse benchmark datasets, and the experimental results have demonstrated the outstanding performance of GEN compared with the state-of-the-art baseline methods in both effectiveness of efficiency. |
| Tasks | Representation Learning |
| Published | 2018-05-19 |
| URL | http://arxiv.org/abs/1805.07508v1 |
| http://arxiv.org/pdf/1805.07508v1.pdf | |
| PWC | https://paperswithcode.com/paper/gen-model-an-alternative-approach-to-deep |
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Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection
| Title | Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection |
| Authors | Yulia S. Chernyshova, Mikhail A. Aliev, Ekaterina S. Gushchanskaia, Alexander V. Sheshkus |
| Abstract | In this paper, we consider the problem of detecting counterfeit identity documents in images captured with smartphones. As the number of documents contain special fonts, we study the applicability of convolutional neural networks (CNNs) for detection of the conformance of the fonts used with the ones, corresponding to the government standards. Here, we use multi-task learning to differentiate samples by both fonts and characters and compare the resulting classifier with its analogue trained for binary font classification. We train neural networks for authenticity estimation of the fonts used in machine-readable zones and ID numbers of the Russian national passport and test them on samples of individual characters acquired from 3238 images of the Russian national passport. Our results show that the usage of multi-task learning increases sensitivity and specificity of the classifier. Moreover, the resulting CNNs demonstrate high generalization ability as they correctly classify fonts which were not present in the training set. We conclude that the proposed method is sufficient for authentication of the fonts and can be used as a part of the forgery detection system for images acquired with a smartphone camera. |
| Tasks | Multi-Task Learning |
| Published | 2018-10-18 |
| URL | http://arxiv.org/abs/1810.08016v1 |
| http://arxiv.org/pdf/1810.08016v1.pdf | |
| PWC | https://paperswithcode.com/paper/optical-font-recognition-in-smartphone |
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A Method Based on Convex Cone Model for Image-Set Classification with CNN Features
| Title | A Method Based on Convex Cone Model for Image-Set Classification with CNN Features |
| Authors | Naoya Sogi, Taku Nakayama, Kazuhiro Fukui |
| Abstract | In this paper, we propose a method for image-set classification based on convex cone models, focusing on the effectiveness of convolutional neural network (CNN) features as inputs. CNN features have non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a set of CNN features by a convex cone and measure the geometric similarity of convex cones for classification. To establish this framework, we sequentially define multiple angles between two convex cones by repeating the alternating least squares method and then define the geometric similarity between the cones using the obtained angles. Moreover, to enhance our method, we introduce a discriminant space, maximizing the between-class variance (gaps) and minimizes the within-class variance of the projected convex cones onto the discriminant space, similar to a Fisher discriminant analysis. Finally, classification is based on the similarity between projected convex cones. The effectiveness of the proposed method was demonstrated experimentally using a private, multi-view hand shape dataset and two public databases. |
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| Published | 2018-05-31 |
| URL | http://arxiv.org/abs/1805.12467v1 |
| http://arxiv.org/pdf/1805.12467v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-method-based-on-convex-cone-model-for-image |
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Pull out all the stops: Textual analysis via punctuation sequences
| Title | Pull out all the stops: Textual analysis via punctuation sequences |
| Authors | Alexandra N. M. Darmon, Marya Bazzi, Sam D. Howison, Mason A. Porter |
| Abstract | Whether enjoying the lucid prose of a favorite author or slogging through some other writer’s cumbersome, heavy-set prattle (full of parentheses, em dashes, compound adjectives, and Oxford commas), readers will notice stylistic signatures not only in word choice and grammar, but also in punctuation itself. Indeed, visual sequences of punctuation from different authors produce marvelously different (and visually striking) sequences. Punctuation is a largely overlooked stylistic feature in “stylometry”, the quantitative analysis of written text. In this paper, we examine punctuation sequences in a corpus of literary documents and ask the following questions: Are the properties of such sequences a distinctive feature of different authors? Is it possible to distinguish literary genres based on their punctuation sequences? Do the punctuation styles of authors evolve over time? Are we on to something interesting in trying to do stylometry without words, or are we full of sound and fury (signifying nothing)? |
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| Published | 2018-12-31 |
| URL | https://arxiv.org/abs/1901.00519v2 |
| https://arxiv.org/pdf/1901.00519v2.pdf | |
| PWC | https://paperswithcode.com/paper/pull-out-all-the-stops-textual-analysis-via |
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A Model for Medical Diagnosis Based on Plantar Pressure
| Title | A Model for Medical Diagnosis Based on Plantar Pressure |
| Authors | Guoxiong Xu, Zhengfei Wang, Hongshi Huang, Wenxin Li, Can Liu, Shilei Liu |
| Abstract | The process of determining which disease or condition explains a person’s symptoms and signs can be very complicated and may be inaccurate in some cases. The general belief is that diagnosing diseases relies on doctors’ keen intuition, rich experience and professional equipment. In this work, we employ ideas from recent advances in plantar pressure research and from the powerful capacity of the convolutional neural network for learning representations. Here, we propose a model using convolutional neural network based on plantar pressure for medical diagnosis. Our model learns a network that maps plantar pressure data to its corresponding medical diagnostic label. We then apply our model to make the medical diagnosis on datasets we collected from cooperative hospital and achieve an accuracy of 98.36%. We demonstrate that the model base on the convolutional neural network is competitive in medical diagnosis. |
| Tasks | Medical Diagnosis |
| Published | 2018-02-28 |
| URL | http://arxiv.org/abs/1802.10316v1 |
| http://arxiv.org/pdf/1802.10316v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-model-for-medical-diagnosis-based-on |
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Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization
| Title | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization |
| Authors | Rui Zhu, Di Niu, Zongpeng Li |
| Abstract | We study stochastic algorithms for solving nonconvex optimization problems with a convex yet possibly nonsmooth regularizer, which find wide applications in many practical machine learning applications. However, compared to asynchronous parallel stochastic gradient descent (AsynSGD), an algorithm targeting smooth optimization, the understanding of the behavior of stochastic algorithms for nonsmooth regularized optimization problems is limited, especially when the objective function is nonconvex. To fill this theoretical gap, in this paper, we propose and analyze asynchronous parallel stochastic proximal gradient (Asyn-ProxSGD) methods for nonconvex problems. We establish an ergodic convergence rate of $O(1/\sqrt{K})$ for the proposed Asyn-ProxSGD, where $K$ is the number of updates made on the model, matching the convergence rate currently known for AsynSGD (for smooth problems). To our knowledge, this is the first work that provides convergence rates of asynchronous parallel ProxSGD algorithms for nonconvex problems. Furthermore, our results are also the first to show the convergence of any stochastic proximal methods without assuming an increasing batch size or the use of additional variance reduction techniques. We implement the proposed algorithms on Parameter Server and demonstrate its convergence behavior and near-linear speedup, as the number of workers increases, on two real-world datasets. |
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| Published | 2018-02-24 |
| URL | http://arxiv.org/abs/1802.08880v3 |
| http://arxiv.org/pdf/1802.08880v3.pdf | |
| PWC | https://paperswithcode.com/paper/asynchronous-stochastic-proximal-methods-for |
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MXNET-MPI: Embedding MPI parallelism in Parameter Server Task Model for scaling Deep Learning
| Title | MXNET-MPI: Embedding MPI parallelism in Parameter Server Task Model for scaling Deep Learning |
| Authors | Amith R Mamidala, Georgios Kollias, Chris Ward, Fausto Artico |
| Abstract | Existing Deep Learning frameworks exclusively use either Parameter Server(PS) approach or MPI parallelism. In this paper, we discuss the drawbacks of such approaches and propose a generic framework supporting both PS and MPI programming paradigms, co-existing at the same time. The key advantage of the new model is to embed the scaling benefits of MPI parallelism into the loosely coupled PS task model. Apart from providing a practical usage model of MPI in cloud, such framework allows for novel communication avoiding algorithms that do parameter averaging in Stochastic Gradient Descent(SGD) approaches. We show how MPI and PS models can synergestically apply algorithms such as Elastic SGD to improve the rate of convergence against existing approaches. These new algorithms directly help scaling SGD clusterwide. Further, we also optimize the critical component of the framework, namely global aggregation or allreduce using a novel concept of tensor collectives. These treat a group of vectors on a node as a single object allowing for the existing single vector algorithms to be directly applicable. We back our claims with sufficient emperical evidence using large scale ImageNet 1K data. Our framework is built upon MXNET but the design is generic and can be adapted to other popular DL infrastructures. |
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| Published | 2018-01-11 |
| URL | http://arxiv.org/abs/1801.03855v1 |
| http://arxiv.org/pdf/1801.03855v1.pdf | |
| PWC | https://paperswithcode.com/paper/mxnet-mpi-embedding-mpi-parallelism-in |
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Real-time cortical simulations: energy and interconnect scaling on distributed systems
| Title | Real-time cortical simulations: energy and interconnect scaling on distributed systems |
| Authors | Francesco Simula, Elena Pastorelli, Pier Stanislao Paolucci, Michele Martinelli, Alessandro Lonardo, Andrea Biagioni, Cristiano Capone, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Luca Pontisso, Piero Vicini, Roberto Ammendola |
| Abstract | We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the speed and energy consumption of processor architectures typical of standard HPC and embedded platforms are compared. We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption. The cost of cortical simulations is quantified using the Joule per synaptic event metric on both architectures. Reaching efficient real-time on large scale cortical simulations is of increasing relevance for both future bio-inspired artificial intelligence applications and for understanding the cognitive functions of the brain, a scientific quest that will require to embed large scale simulations into highly complex virtual or real worlds. This work stands at the crossroads between the WaveScalES experiment in the Human Brain Project (HBP), which includes the objective of large scale thalamo-cortical simulations of brain states and their transitions, and the ExaNeSt and EuroExa projects, that investigate the design of an ARM-based, low-power High Performance Computing (HPC) architecture with a dedicated interconnect scalable to million of cores; simulation of deep sleep Slow Wave Activity (SWA) and Asynchronous aWake (AW) regimes expressed by thalamo-cortical models are among their benchmarks. |
| Tasks | |
| Published | 2018-12-12 |
| URL | https://arxiv.org/abs/1812.04974v4 |
| https://arxiv.org/pdf/1812.04974v4.pdf | |
| PWC | https://paperswithcode.com/paper/real-time-cortical-simulations-energy-and |
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Using Textual Summaries to Describe a Set of Products
| Title | Using Textual Summaries to Describe a Set of Products |
| Authors | Kittipitch Kuptavanich |
| Abstract | When customers are faced with the task of making a purchase in an unfamiliar product domain, it might be useful to provide them with an overview of the product set to help them understand what they can expect. In this paper we present and evaluate a method to summarise sets of products in natural language, focusing on the price range, common product features across the set, and product features that impact on price. In our study, participants reported that they found our summaries useful, but we found no evidence that the summaries influenced the selections made by participants. |
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| Published | 2018-07-16 |
| URL | http://arxiv.org/abs/1807.06008v1 |
| http://arxiv.org/pdf/1807.06008v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-textual-summaries-to-describe-a-set-of |
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A Note on Spectral Clustering and SVD of Graph Data
| Title | A Note on Spectral Clustering and SVD of Graph Data |
| Authors | Ziwei Zhang |
| Abstract | Spectral clustering and Singular Value Decomposition (SVD) are both widely used technique for analyzing graph data. In this note, I will present their connections using simple linear algebra, aiming to provide some in-depth understanding for future research. |
| Tasks | |
| Published | 2018-09-27 |
| URL | http://arxiv.org/abs/1809.11029v1 |
| http://arxiv.org/pdf/1809.11029v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-note-on-spectral-clustering-and-svd-of |
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Multivariate normal mixture modeling, clustering and classification with the rebmix package
| Title | Multivariate normal mixture modeling, clustering and classification with the rebmix package |
| Authors | Marko Nagode |
| Abstract | The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a known number of components, numbers of observations and component parameters, how to estimate the number of components, component weights and component parameters and how to predict cluster and class membership based upon a model trained by the REBMIX algorithm. The accompanying plotting, bootstrapping and other features of the package are dealt with, too. For demonstration purpose a multivariate normal dataset with unrestricted variance-covariance matrices is studied. |
| Tasks | |
| Published | 2018-01-26 |
| URL | http://arxiv.org/abs/1801.08788v1 |
| http://arxiv.org/pdf/1801.08788v1.pdf | |
| PWC | https://paperswithcode.com/paper/multivariate-normal-mixture-modeling |
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Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms
| Title | Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms |
| Authors | Roman V. Yampolskiy |
| Abstract | In this paper, we review the state-of-the-art results in evolutionary computation and observe that we do not evolve non trivial software from scratch and with no human intervention. A number of possible explanations are considered, but we conclude that computational complexity of the problem prevents it from being solved as currently attempted. A detailed analysis of necessary and available computational resources is provided to support our findings. |
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| Published | 2018-10-12 |
| URL | http://arxiv.org/abs/1810.07074v1 |
| http://arxiv.org/pdf/1810.07074v1.pdf | |
| PWC | https://paperswithcode.com/paper/why-we-do-not-evolve-software-analysis-of |
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How to aggregate Top-lists: Approximation algorithms via scores and average ranks
| Title | How to aggregate Top-lists: Approximation algorithms via scores and average ranks |
| Authors | Claire Mathieu, Simon Mauras |
| Abstract | A top-list is a possibly incomplete ranking of elements: only a subset of the elements are ranked, with all unranked elements tied for last. Top-list aggregation, a generalization of the well-known rank aggregation problem, takes as input a collection of top-lists and aggregates them into a single complete ranking, aiming to minimize the number of upsets (pairs ranked in opposite order in the input and in the output). In this paper, we give simple approximation algorithms for top-list aggregation. * We generalize the footrule algorithm for rank aggregation. * Using inspiration from approval voting, we define the score of an element as the frequency with which it is ranked, i.e. appears in an input top-list. We reinterpret Ailon’s RepeatChoice algorithm for top-list aggregation using the score of an element and its average rank given that it is ranked. * Using average ranks, we generalize and analyze Borda’s algorithm for rank aggregation. * We design a simple 2-phase variant of the Generalized Borda’s algorithm, roughly sorting by scores and breaking ties by average ranks. * We then design another 2-phase variant in which in order to break ties we use, as a black box, the Mathieu-Schudy PTAS for rank aggregation, yielding a PTAS for top-list aggregation. * Finally, we discuss the special case in which all input lists have constant length. |
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| Published | 2018-11-05 |
| URL | https://arxiv.org/abs/1811.01537v2 |
| https://arxiv.org/pdf/1811.01537v2.pdf | |
| PWC | https://paperswithcode.com/paper/how-to-aggregate-top-lists-approximation |
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