Paper Group ANR 587
Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations. Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments. EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure. Modifying Optimal SAT-based Approach to Multi-agent Path-find …
Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations
| Title | Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations |
| Authors | Hassane Abouaïssa, Ola Alhaj Hasan, Cédric Join, Michel Fliess, Didier Defer |
| Abstract | The model-based control of building heating systems for energy saving encounters severe physical, mathematical and calibration difficulties in the numerous attempts that has been published until now. This topic is addressed here via a new model-free control setting, where the need of any mathematical description disappears. Several convincing computer simulations are presented. Comparisons with classic PI controllers and flatness-based predictive control are provided. |
| Tasks | Calibration |
| Published | 2017-08-12 |
| URL | http://arxiv.org/abs/1708.03800v2 |
| http://arxiv.org/pdf/1708.03800v2.pdf | |
| PWC | https://paperswithcode.com/paper/energy-saving-for-building-heating-via-a |
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Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments
| Title | Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments |
| Authors | Hang Ma, Jingxing Yang, Liron Cohen, T. K. Satish Kumar, Sven Koenig |
| Abstract | Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. In video games, agents of different types often form teams. In this paper, we demonstrate the usefulness of MAPF algorithms from artificial intelligence for moving such non-homogeneous teams in congested video game environments. |
| Tasks | Multi-Agent Path Finding |
| Published | 2017-10-04 |
| URL | http://arxiv.org/abs/1710.01447v1 |
| http://arxiv.org/pdf/1710.01447v1.pdf | |
| PWC | https://paperswithcode.com/paper/feasibility-study-moving-non-homogeneous |
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EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure
| Title | EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure |
| Authors | Jialong Shi, Qingfu Zhang, Edward Tsang |
| Abstract | Local search is a basic building block in memetic algorithms. Guided Local Search (GLS) can improve the efficiency of local search. By changing the guide function, GLS guides a local search to escape from locally optimal solutions and find better solutions. The key component of GLS is its penalizing mechanism which determines which feature is selected to penalize when the search is trapped in a locally optimal solution. The original GLS penalizing mechanism only makes use of the cost and the current penalty value of each feature. It is well known that many combinatorial optimization problems have a big valley structure, i.e., the better a solution is, the more the chance it is closer to a globally optimal solution. This paper proposes to use big valley structure assumption to improve the GLS penalizing mechanism. An improved GLS algorithm called Elite Biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution. We have systematically tested the proposed algorithm on the symmetric traveling salesman problem. Experimental results show that EB-GLS is significantly better than GLS. |
| Tasks | Combinatorial Optimization |
| Published | 2017-09-22 |
| URL | http://arxiv.org/abs/1709.07576v1 |
| http://arxiv.org/pdf/1709.07576v1.pdf | |
| PWC | https://paperswithcode.com/paper/eb-gls-an-improved-guided-local-search-based |
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Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants
| Title | Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants |
| Authors | Pavel Surynek, Ariel Felner, Roni Stern, Eli Boyarski |
| Abstract | In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. In this paper we focus on finding suboptimal solutions for MAPF for the sum-of-costs variant. Recently, a SAT-based approached was developed to solve this problem and proved beneficial in many cases when compared to other search-based solvers. In this paper, we present SAT-based unbounded- and bounded-suboptimal algorithms and compare them to relevant algorithms. Experimental results show that in many case the SAT-based solver significantly outperforms the search-based solvers. |
| Tasks | Multi-Agent Path Finding |
| Published | 2017-07-02 |
| URL | http://arxiv.org/abs/1707.00228v1 |
| http://arxiv.org/pdf/1707.00228v1.pdf | |
| PWC | https://paperswithcode.com/paper/modifying-optimal-sat-based-approach-to-multi |
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Rapid Randomized Restarts for Multi-Agent Path Finding Solvers
| Title | Rapid Randomized Restarts for Multi-Agent Path Finding Solvers |
| Authors | Liron Cohen, Glenn Wagner, T. K. Satish Kumar, Howie Choset, Sven Koenig |
| Abstract | Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in artificial intelligence and robotics. It has many real-world applications for which existing MAPF solvers use various heuristics. However, these solvers are deterministic and perform poorly on “hard” instances typically characterized by many agents interfering with each other in a small region. In this paper, we enhance MAPF solvers with randomization and observe that they exhibit heavy-tailed distributions of runtimes on hard instances. This leads us to develop simple rapid randomized restart (RRR) strategies with the intuition that, given a hard instance, multiple short runs have a better chance of solving it compared to one long run. We validate this intuition through experiments and show that our RRR strategies indeed boost the performance of state-of-the-art MAPF solvers such as iECBS and M*. |
| Tasks | Multi-Agent Path Finding |
| Published | 2017-06-08 |
| URL | http://arxiv.org/abs/1706.02794v1 |
| http://arxiv.org/pdf/1706.02794v1.pdf | |
| PWC | https://paperswithcode.com/paper/rapid-randomized-restarts-for-multi-agent |
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Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
| Title | Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks |
| Authors | Hang Ma, Jiaoyang Li, T. K. Satish Kumar, Sven Koenig |
| Abstract | The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm. |
| Tasks | Multi-Agent Path Finding |
| Published | 2017-05-30 |
| URL | http://arxiv.org/abs/1705.10868v1 |
| http://arxiv.org/pdf/1705.10868v1.pdf | |
| PWC | https://paperswithcode.com/paper/lifelong-multi-agent-path-finding-for-online |
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Anatomical Pattern Analysis for decoding visual stimuli in human brains
| Title | Anatomical Pattern Analysis for decoding visual stimuli in human brains |
| Authors | Muhammad Yousefnezhad, Daoqiang Zhang |
| Abstract | Background: A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noise in the extracted features and increasing the performance of prediction. Methods: In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multiclass prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Results and Conclusions: Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods. |
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| Published | 2017-10-05 |
| URL | http://arxiv.org/abs/1710.02113v1 |
| http://arxiv.org/pdf/1710.02113v1.pdf | |
| PWC | https://paperswithcode.com/paper/anatomical-pattern-analysis-for-decoding |
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Sampling Without Compromising Accuracy in Adaptive Data Analysis
| Title | Sampling Without Compromising Accuracy in Adaptive Data Analysis |
| Authors | Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein |
| Abstract | In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms. This is important to do when both the datasets and the number of queries asked are very large. In particular, we describe a mechanism that provides a polynomial speed-up per query over previous mechanisms, without needing to increase the total amount of data required to maintain the same generalization error as before. We prove that this speed-up holds for arbitrary statistical queries. We also provide an even faster method for achieving statistically-meaningful responses wherein the mechanism is only allowed to see a constant number of samples from the data per query. Finally, we show that our general results yield a simple, fast, and unified approach for adaptively optimizing convex and strongly convex functions over a dataset. |
| Tasks | |
| Published | 2017-09-28 |
| URL | https://arxiv.org/abs/1709.09778v3 |
| https://arxiv.org/pdf/1709.09778v3.pdf | |
| PWC | https://paperswithcode.com/paper/sampling-without-compromising-accuracy-in |
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Joint Image Filtering with Deep Convolutional Networks
| Title | Joint Image Filtering with Deep Convolutional Networks |
| Authors | Yijun Li, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang |
| Abstract | Joint image filters leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution. Existing methods either rely on various explicit filter constructions or hand-designed objective functions, thereby making it difficult to understand, improve, and accelerate these filters in a coherent framework. In this paper, we propose a learning-based approach for constructing joint filters based on Convolutional Neural Networks. In contrast to existing methods that consider only the guidance image, the proposed algorithm can selectively transfer salient structures that are consistent with both guidance and target images. We show that the model trained on a certain type of data, e.g., RGB and depth images, generalizes well to other modalities, e.g., flash/non-Flash and RGB/NIR images. We validate the effectiveness of the proposed joint filter through extensive experimental evaluations with state-of-the-art methods. |
| Tasks | |
| Published | 2017-10-11 |
| URL | http://arxiv.org/abs/1710.04200v5 |
| http://arxiv.org/pdf/1710.04200v5.pdf | |
| PWC | https://paperswithcode.com/paper/joint-image-filtering-with-deep-convolutional |
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Signal-based Bayesian Seismic Monitoring
| Title | Signal-based Bayesian Seismic Monitoring |
| Authors | David A. Moore, Stuart J. Russell |
| Abstract | Detecting weak seismic events from noisy sensors is a difficult perceptual task. We formulate this task as Bayesian inference and propose a generative model of seismic events and signals across a network of spatially distributed stations. Our system, SIGVISA, is the first to directly model seismic waveforms, allowing it to incorporate a rich representation of the physics underlying the signal generation process. We use Gaussian processes over wavelet parameters to predict detailed waveform fluctuations based on historical events, while degrading smoothly to simple parametric envelopes in regions with no historical seismicity. Evaluating on data from the western US, we recover three times as many events as previous work, and reduce mean location errors by a factor of four while greatly increasing sensitivity to low-magnitude events. |
| Tasks | Bayesian Inference, Gaussian Processes, Seismic Detection |
| Published | 2017-03-02 |
| URL | http://arxiv.org/abs/1703.00561v1 |
| http://arxiv.org/pdf/1703.00561v1.pdf | |
| PWC | https://paperswithcode.com/paper/signal-based-bayesian-seismic-monitoring |
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A network approach to topic models
| Title | A network approach to topic models |
| Authors | Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann |
| Abstract | One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning approach which infers the latent topical structure of a collection of documents. Despite their success — in particular of its most widely used variant called Latent Dirichlet Allocation (LDA) — and numerous applications in sociology, history, and linguistics, topic models are known to suffer from severe conceptual and practical problems, e.g. a lack of justification for the Bayesian priors, discrepancies with statistical properties of real texts, and the inability to properly choose the number of topics. Here we obtain a fresh view on the problem of identifying topical structures by relating it to the problem of finding communities in complex networks. This is achieved by representing text corpora as bipartite networks of documents and words. By adapting existing community-detection methods – using a stochastic block model (SBM) with non-parametric priors – we obtain a more versatile and principled framework for topic modeling (e.g., it automatically detects the number of topics and hierarchically clusters both the words and documents). The analysis of artificial and real corpora demonstrates that our SBM approach leads to better topic models than LDA in terms of statistical model selection. More importantly, our work shows how to formally relate methods from community detection and topic modeling, opening the possibility of cross-fertilization between these two fields. |
| Tasks | Community Detection, Model Selection, Topic Models |
| Published | 2017-08-04 |
| URL | http://arxiv.org/abs/1708.01677v2 |
| http://arxiv.org/pdf/1708.01677v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-network-approach-to-topic-models |
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Skin Lesion Classification using Class Activation Map
| Title | Skin Lesion Classification using Class Activation Map |
| Authors | Xi Jia, Linlin Shen |
| Abstract | We proposed a two stage framework with only one network to analyze skin lesion images, we firstly trained a convolutional network to classify these images, and cropped the import regions which the network has the maximum activation value. In the second stage, we retrained this CNN with the image regions extracted from stage one and output the final probabilities. The two stage framework achieved a mean AUC of 0.857 in ISIC-2017 skin lesion validation set and is 0.04 higher than that of the original inputs, 0.821. |
| Tasks | Skin Lesion Classification |
| Published | 2017-03-03 |
| URL | http://arxiv.org/abs/1703.01053v1 |
| http://arxiv.org/pdf/1703.01053v1.pdf | |
| PWC | https://paperswithcode.com/paper/skin-lesion-classification-using-class |
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Weight-Based Variable Ordering in the Context of High-Level Consistencies
| Title | Weight-Based Variable Ordering in the Context of High-Level Consistencies |
| Authors | Robert J. Woodward, Berthe Y. Choueiry |
| Abstract | Dom/wdeg is one of the best performing heuristics for dynamic variable ordering in backtrack search [Boussemart et al., 2004]. As originally defined, this heuristic increments the weight of the constraint that causes a domain wipeout (i.e., a dead-end) when enforcing arc consistency during search. “The process of weighting constraints with dom/wdeg is not defined when more than one constraint lead to a domain wipeout [Vion et al., 2011].” In this paper, we investigate how weights should be updated in the context of two high-level consistencies, namely, singleton (POAC) and relational consistencies (RNIC). We propose, analyze, and empirically evaluate several strategies for updating the weights. We statistically compare the proposed strategies and conclude with our recommendations. |
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| Published | 2017-11-02 |
| URL | http://arxiv.org/abs/1711.00909v1 |
| http://arxiv.org/pdf/1711.00909v1.pdf | |
| PWC | https://paperswithcode.com/paper/weight-based-variable-ordering-in-the-context |
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Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models
| Title | Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models |
| Authors | Gal Shulkind, Lior Horesh, Haim Avron |
| Abstract | We consider a class of misspecified dynamical models where the governing term is only approximately known. Under the assumption that observations of the system’s evolution are accessible for various initial conditions, our goal is to infer a non-parametric correction to the misspecified driving term such as to faithfully represent the system dynamics and devise system evolution predictions for unobserved initial conditions. We model the unknown correction term as a Gaussian Process and analyze the problem of efficient experimental design to find an optimal correction term under constraints such as a limited experimental budget. We suggest a novel formulation for experimental design for this Gaussian Process and show that approximately optimal (up to a constant factor) designs may be efficiently derived by utilizing results from the literature on submodular optimization. Our numerical experiments exemplify the effectiveness of these techniques. |
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| Published | 2017-05-02 |
| URL | http://arxiv.org/abs/1705.00956v3 |
| http://arxiv.org/pdf/1705.00956v3.pdf | |
| PWC | https://paperswithcode.com/paper/experimental-design-for-non-parametric |
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Increasing Papers’ Discoverability with Precise Semantic Labeling: the sci.AI Platform
| Title | Increasing Papers’ Discoverability with Precise Semantic Labeling: the sci.AI Platform |
| Authors | Roman Gurinovich, Alexander Pashuk, Yuriy Petrovskiy, Alex Dmitrievskij, Oleg Kuryan, Alexei Scerbacov, Antonia Tiggre, Elena Moroz, Yuri Nikolsky |
| Abstract | The number of published findings in biomedicine increases continually. At the same time, specifics of the domain’s terminology complicates the task of relevant publications retrieval. In the current research, we investigate influence of terms’ variability and ambiguity on a paper’s likelihood of being retrieved. We obtained statistics that demonstrate significance of the issue and its challenges, followed by presenting the sci.AI platform, which allows precise terms labeling as a resolution. |
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| Published | 2017-05-02 |
| URL | http://arxiv.org/abs/1705.08321v1 |
| http://arxiv.org/pdf/1705.08321v1.pdf | |
| PWC | https://paperswithcode.com/paper/increasing-papers-discoverability-with |
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