Paper Group ANR 237
On the Universality of Memcomputing Machines. Weight-based Fish School Search algorithm for Many-Objective Optimization. An Unsupervised Game-Theoretic Approach to Saliency Detection. Logic Programming approaches for routing fault-free and maximally-parallel Wavelength Routed Optical Networks on Chip (Application paper). A Deep Learning Model for T …
On the Universality of Memcomputing Machines
| Title | On the Universality of Memcomputing Machines |
| Authors | Yan Ru Pei, Fabio L. Traversa, Massimiliano Di Ventra |
| Abstract | Universal memcomputing machines (UMMs) [IEEE Trans. Neural Netw. Learn. Syst. 26, 2702 (2015)] represent a novel computational model in which memory (time non-locality) accomplishes both tasks of storing and processing of information. UMMs have been shown to be Turing-complete, namely they can simulate any Turing machine. In this paper, using set theory and cardinality arguments, we compare them with liquid-state machines (or “reservoir computing”) and quantum machines (“quantum computing”). We show that UMMs can simulate both types of machines, hence they are both “liquid-” or “reservoir-complete” and “quantum-complete”. Of course, these statements pertain only to the type of problems these machines can solve, and not to the amount of resources required for such simulations. Nonetheless, the method presented here provides a general framework in which to describe the relation between UMMs and any other type of computational model. |
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| Published | 2017-12-23 |
| URL | https://arxiv.org/abs/1712.08702v2 |
| https://arxiv.org/pdf/1712.08702v2.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-universality-of-memcomputing-machines |
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Weight-based Fish School Search algorithm for Many-Objective Optimization
| Title | Weight-based Fish School Search algorithm for Many-Objective Optimization |
| Authors | F. B. Lima Neto, I. M. C. Albuquerque, J. B. Monteiro Filho |
| Abstract | Optimization problems with more than one objective consist in a very attractive topic for researchers due to its applicability in real-world situations. Over the years, the research effort in the Computational Intelligence field resulted in algorithms able to achieve good results by solving problems with more than one conflicting objective. However, these techniques do not exhibit the same performance as the number of objectives increases and become greater than 3. This paper proposes an adaptation of the metaheuristic Fish School Search to solve optimization problems with many objectives. This adaptation is based on the division of the candidate solutions in clusters that are specialized in solving a single-objective problem generated by the decomposition of the original problem. For this, we used concepts and ideas often employed by state-of-the-art algorithms, namely: (i) reference points and lines in the objectives space; (ii) clustering process; and (iii) the decomposition technique Penalty-based Boundary Intersection. The proposed algorithm was compared with two state-of-the-art bio-inspired algorithms. Moreover, a version of the proposed technique tailored to solve multi-modal problems was also presented. The experiments executed have shown that the performance obtained by both versions is competitive with state-of-the-art results. |
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| Published | 2017-08-16 |
| URL | http://arxiv.org/abs/1708.04745v3 |
| http://arxiv.org/pdf/1708.04745v3.pdf | |
| PWC | https://paperswithcode.com/paper/weight-based-fish-school-search-algorithm-for |
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An Unsupervised Game-Theoretic Approach to Saliency Detection
| Title | An Unsupervised Game-Theoretic Approach to Saliency Detection |
| Authors | Yu Zeng, Huchuan Lu, Ali Borji, Mengyang Feng |
| Abstract | We propose a novel unsupervised game-theoretic salient object detection algorithm that does not require labeled training data. First, saliency detection problem is formulated as a non-cooperative game, hereinafter referred to as Saliency Game, in which image regions are players who choose to be “background” or “foreground” as their pure strategies. A payoff function is constructed by exploiting multiple cues and combining complementary features. Saliency maps are generated according to each region’s strategy in the Nash equilibrium of the proposed Saliency Game. Second, we explore the complementary relationship between color and deep features and propose an Iterative Random Walk algorithm to combine saliency maps produced by the Saliency Game using different features. Iterative random walk allows sharing information across feature spaces, and detecting objects that are otherwise very hard to detect. Extensive experiments over 6 challenging datasets demonstrate the superiority of our proposed unsupervised algorithm compared to several state of the art supervised algorithms. |
| Tasks | Object Detection, Saliency Detection, Salient Object Detection |
| Published | 2017-08-08 |
| URL | http://arxiv.org/abs/1708.02476v1 |
| http://arxiv.org/pdf/1708.02476v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-unsupervised-game-theoretic-approach-to |
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Logic Programming approaches for routing fault-free and maximally-parallel Wavelength Routed Optical Networks on Chip (Application paper)
| Title | Logic Programming approaches for routing fault-free and maximally-parallel Wavelength Routed Optical Networks on Chip (Application paper) |
| Authors | Marco Gavanelli, Maddalena Nonato, Andrea Peano, Davide Bertozzi |
| Abstract | One promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip (ONoCs). Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. The underlying technology, only quickly mentioned in that paper, is Answer Set Programming (ASP). In this work, we detail the ASP approach we used to solve such problem. Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint Logic Programming on Finite Domains (CLP(FD)). This paper is under consideration for possible publication on Theory and Practice of Logic Programming. |
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| Published | 2017-07-18 |
| URL | http://arxiv.org/abs/1707.05858v1 |
| http://arxiv.org/pdf/1707.05858v1.pdf | |
| PWC | https://paperswithcode.com/paper/logic-programming-approaches-for-routing |
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A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data
| Title | A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data |
| Authors | Wenwen Tu, Feng Xiao, Liping Fu, Guangyuan Pan |
| Abstract | This study proposes a Deep Belief Network model to classify traffic flow states. The model is capable of processing massive, high-density, and noise-contaminated data sets generated from smartphone sensors. The statistical features of Vehicle acceleration, angular acceleration, and GPS speed data, recorded by smartphone software, are analyzed, and then used as input for traffic flow state classification. Data from a five-day experiment is used to train and test the proposed model. A total of 747,856 sets of data are generated and used for both traffic flow states classification and sensitivity analysis of input variables. The result shows that the proposed Deep Belief Network model is superior to traditional machine learning methods in both classification performance and computational efficiency. |
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| Published | 2017-09-26 |
| URL | http://arxiv.org/abs/1709.08802v1 |
| http://arxiv.org/pdf/1709.08802v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-deep-learning-model-for-traffic-flow-state |
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Estimation of interventional effects of features on prediction
| Title | Estimation of interventional effects of features on prediction |
| Authors | Patrick Blöbaum, Shohei Shimizu |
| Abstract | The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models with meaningful parameters, the causal relationships between the predictors and the actual prediction have not been considered. Here, we connect the underlying causal structure of a data generation process and the causal structure of a prediction mechanism. To achieve this, we propose a framework that identifies the feature with the greatest causal influence on the prediction and estimates the necessary causal intervention of a feature such that a desired prediction is obtained. The general concept of the framework has no restrictions regarding data linearity; however, we focus on an implementation for linear data here. The framework applicability is evaluated using artificial data and demonstrated using real-world data. |
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| Published | 2017-09-03 |
| URL | http://arxiv.org/abs/1709.00776v1 |
| http://arxiv.org/pdf/1709.00776v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimation-of-interventional-effects-of |
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Strategy Synthesis in POMDPs via Game-Based Abstractions
| Title | Strategy Synthesis in POMDPs via Game-Based Abstractions |
| Authors | Leonore Winterer, Sebastian Junges, Ralf Wimmer, Nils Jansen, Ufuk Topcu, Joost-Pieter Katoen, Bernd Becker |
| Abstract | We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications. Verification and strategy synthesis for POMDPs are, however, computationally intractable in general. We alleviate this difficulty by focusing on planning applications and exploiting typical structural properties of such scenarios; for instance, we assume that the agent has the ability to observe its own position inside an environment. We propose an abstraction refinement framework which turns such a POMDP model into a (fully observable) probabilistic two-player game (PG). For the obtained PGs, efficient verification and synthesis tools allow to determine strategies with optimal safety and performance measures, which approximate optimal schedulers on the POMDP. If the approximation is too coarse to satisfy the given specifications, an refinement scheme improves the computed strategies. As a running example, we use planning problems where an agent moves inside an environment with randomly moving obstacles and restricted observability. We demonstrate that the proposed method advances the state of the art by solving problems several orders-of-magnitude larger than those that can be handled by existing POMDP solvers. Furthermore, this method gives guarantees on safety constraints, which is not supported by the majority of the existing solvers. |
| Tasks | Motion Planning |
| Published | 2017-08-14 |
| URL | https://arxiv.org/abs/1708.04236v2 |
| https://arxiv.org/pdf/1708.04236v2.pdf | |
| PWC | https://paperswithcode.com/paper/motion-planning-under-partial-observability |
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Gradient-based Regularization Parameter Selection for Problems with Non-smooth Penalty Functions
| Title | Gradient-based Regularization Parameter Selection for Problems with Non-smooth Penalty Functions |
| Authors | Jean Feng, Noah Simon |
| Abstract | In high-dimensional and/or non-parametric regression problems, regularization (or penalization) is used to control model complexity and induce desired structure. Each penalty has a weight parameter that indicates how strongly the structure corresponding to that penalty should be enforced. Typically the parameters are chosen to minimize the error on a separate validation set using a simple grid search or a gradient-free optimization method. It is more efficient to tune parameters if the gradient can be determined, but this is often difficult for problems with non-smooth penalty functions. Here we show that for many penalized regression problems, the validation loss is actually smooth almost-everywhere with respect to the penalty parameters. We can therefore apply a modified gradient descent algorithm to tune parameters. Through simulation studies on example regression problems, we find that increasing the number of penalty parameters and tuning them using our method can decrease the generalization error. |
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| Published | 2017-03-28 |
| URL | http://arxiv.org/abs/1703.09813v1 |
| http://arxiv.org/pdf/1703.09813v1.pdf | |
| PWC | https://paperswithcode.com/paper/gradient-based-regularization-parameter |
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Deterministic Quantum Annealing Expectation-Maximization Algorithm
| Title | Deterministic Quantum Annealing Expectation-Maximization Algorithm |
| Authors | Hideyuki Miyahara, Koji Tsumura, Yuki Sughiyama |
| Abstract | Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field of physics, quantum annealing (QA) was proposed as a novel optimization approach. Motivated by QA, we propose a quantum annealing extension of EM, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm. We also discuss its advantage in terms of the path integral formulation. Furthermore, by employing numerical simulations, we illustrate how it works in MLE and show that DQAEM outperforms EM. |
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| Published | 2017-04-19 |
| URL | http://arxiv.org/abs/1704.05822v1 |
| http://arxiv.org/pdf/1704.05822v1.pdf | |
| PWC | https://paperswithcode.com/paper/deterministic-quantum-annealing-expectation |
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Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering
| Title | Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering |
| Authors | Shafiq Joty, Preslav Nakov, Lluís Màrquez, Israa Jaradat |
| Abstract | We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system. |
| Tasks | Community Question Answering, Question Answering, Question Similarity |
| Published | 2017-06-21 |
| URL | http://arxiv.org/abs/1706.06749v1 |
| http://arxiv.org/pdf/1706.06749v1.pdf | |
| PWC | https://paperswithcode.com/paper/cross-language-learning-with-adversarial-1 |
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Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network
| Title | Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network |
| Authors | Jen-Cheng Hou, Syu-Siang Wang, Ying-Hui Lai, Yu Tsao, Hsiu-Wen Chang, Hsin-Min Wang |
| Abstract | Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual data are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at the output layer. The AVDCNN model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process. |
| Tasks | Speech Enhancement |
| Published | 2017-09-01 |
| URL | http://arxiv.org/abs/1709.00944v2 |
| http://arxiv.org/pdf/1709.00944v2.pdf | |
| PWC | https://paperswithcode.com/paper/audio-visual-speech-enhancement-based-on |
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Accessible Melanoma Detection using Smartphones and Mobile Image Analysis
| Title | Accessible Melanoma Detection using Smartphones and Mobile Image Analysis |
| Authors | T. -T. Do, T. Hoang, V. Pomponiu, Y. Zhou, Z. Chen, N. -M. Cheung, D. Koh, A. Tan, S. -H. Tan |
| Abstract | We investigate the design of an entire mobile imaging system for early detection of melanoma. Different from previous work, we focus on smartphone-captured visible light images. Our design addresses two major challenges. First, images acquired using a smartphone under loosely-controlled environmental conditions may be subject to various distortions, and this makes melanoma detection more difficult. Second, processing performed on a smartphone is subject to stringent computation and memory constraints. In our work, we propose a detection system that is optimized to run entirely on the resource-constrained smartphone. Our system intends to localize the skin lesion by combining a lightweight method for skin detection with a hierarchical segmentation approach using two fast segmentation methods. Moreover, we study an extensive set of image features and propose new numerical features to characterize a skin lesion. Furthermore, we propose an improved feature selection algorithm to determine a small set of discriminative features used by the final lightweight system. In addition, we study the human-computer interface (HCI) design to understand the usability and acceptance issues of the proposed system. |
| Tasks | Feature Selection |
| Published | 2017-11-27 |
| URL | http://arxiv.org/abs/1711.09553v2 |
| http://arxiv.org/pdf/1711.09553v2.pdf | |
| PWC | https://paperswithcode.com/paper/accessible-melanoma-detection-using |
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Topic Based Sentiment Analysis Using Deep Learning
| Title | Topic Based Sentiment Analysis Using Deep Learning |
| Authors | Sharath T. S., Shubhangi Tandon |
| Abstract | In this paper , we tackle Sentiment Analysis conditioned on a Topic in Twitter data using Deep Learning . We propose a 2-tier approach : In the first phase we create our own Word Embeddings and see that they do perform better than state-of-the-art embeddings when used with standard classifiers. We then perform inference on these embeddings to learn more about a word with respect to all the topics being considered, and also the top n-influencing words for each topic. In the second phase we use these embeddings to predict the sentiment of the tweet with respect to a given topic, and all other topics under discussion. |
| Tasks | Sentiment Analysis, Word Embeddings |
| Published | 2017-10-28 |
| URL | http://arxiv.org/abs/1710.10498v1 |
| http://arxiv.org/pdf/1710.10498v1.pdf | |
| PWC | https://paperswithcode.com/paper/topic-based-sentiment-analysis-using-deep |
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Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection
| Title | Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection |
| Authors | Yimian Dai, Yiquan Wu |
| Abstract | Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters. |
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| Published | 2017-03-27 |
| URL | http://arxiv.org/abs/1703.09157v1 |
| http://arxiv.org/pdf/1703.09157v1.pdf | |
| PWC | https://paperswithcode.com/paper/reweighted-infrared-patch-tensor-model-with |
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Learning the Structure of Generative Models without Labeled Data
| Title | Learning the Structure of Generative Models without Labeled Data |
| Authors | Stephen H. Bach, Bryan He, Alexander Ratner, Christopher Ré |
| Abstract | Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale from weak supervision sources. The generative model’s dependency structure directly affects the quality of the estimated labels, but selecting a structure automatically without any labeled data is a distinct challenge. We propose a structure estimation method that maximizes the $\ell_1$-regularized marginal pseudolikelihood of the observed data. Our analysis shows that the amount of unlabeled data required to identify the true structure scales sublinearly in the number of possible dependencies for a broad class of models. Simulations show that our method is 100$\times$ faster than a maximum likelihood approach and selects $1/4$ as many extraneous dependencies. We also show that our method provides an average of 1.5 F1 points of improvement over existing, user-developed information extraction applications on real-world data such as PubMed journal abstracts. |
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| Published | 2017-03-02 |
| URL | http://arxiv.org/abs/1703.00854v2 |
| http://arxiv.org/pdf/1703.00854v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-the-structure-of-generative-models |
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