October 16, 2019

2948 words 14 mins read

Paper Group ANR 1001

Smart energy management as a means towards improved energy efficiency. Bounded Rational Decision-Making with Adaptive Neural Network Priors. Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping. Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search. Molecula …

Smart energy management as a means towards improved energy efficiency


Title	Smart energy management as a means towards improved energy efficiency
Authors	Dylan te Lindert, Cláudio Rebelo de Sá, Carlos Soares, Arno J. Knobbe
Abstract	The costs associated with refrigerator equipment often represent more than half of the total energy costs in supermarkets. This presents a good motivation for running these systems efficiently. In this study, we investigate different ways to construct a reference behavior, which can serve as a baseline for judging the performance of energy consumption. We used 3 distinct learning models: Multiple Linear Regression, Random Forests, and Artificial Neural Networks. During our experiments we used a variation of the sliding window method in combination with learning curves. We applied this approach on five different supermarkets, across Portugal. We are able to create baselines using off-the-shelf data mining techniques. Moreover, we found a way to create them based on short term historical data. We believe that our research will serve as a base for future studies, for which we provide interesting directions.
Tasks
Published	2018-02-08
URL	http://arxiv.org/abs/1802.04128v1
PDF	http://arxiv.org/pdf/1802.04128v1.pdf
PWC	https://paperswithcode.com/paper/smart-energy-management-as-a-means-towards
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Bounded Rational Decision-Making with Adaptive Neural Network Priors


Title	Bounded Rational Decision-Making with Adaptive Neural Network Priors
Authors	Heinke Hihn, Sebastian Gottwald, Daniel A. Braun
Abstract	Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power. In particular, information theoretic bounded rationality models formalize resource constraints abstractly in terms of relative Shannon information, namely the Kullback-Leibler Divergence between the agents’ prior and posterior policy. Between prior and posterior lies an anytime deliberation process that can be instantiated by sample-based evaluations of the utility function through Markov Chain Monte Carlo (MCMC) optimization. The most simple model assumes a fixed prior and can relate abstract information-theoretic processing costs to the number of sample evaluations. However, more advanced models would also address the question of learning, that is how the prior is adapted over time such that generated prior proposals become more efficient. In this work we investigate generative neural networks as priors that are optimized concurrently with anytime sample-based decision-making processes such as MCMC. We evaluate this approach on toy examples.
Tasks	Decision Making
Published	2018-09-04
URL	http://arxiv.org/abs/1809.01575v1
PDF	http://arxiv.org/pdf/1809.01575v1.pdf
PWC	https://paperswithcode.com/paper/bounded-rational-decision-making-with
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Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping


Title	Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping
Authors	Peiyuan Liao
Abstract	The manipulator workspace mapping is an important problem in robotics and has attracted significant attention in the community. However, most of the pre-existing algorithms have expensive time complexity due to the reliance on sophisticated kinematic equations. To solve this problem, this paper introduces subspace learning (SL), a variant of subspace embedding, where a set of robot and scope parameters is mapped to the corresponding workspace by a deep neural network (DNN). Trained on a large dataset of around $\mathbf{6\times 10^4}$ samples obtained from a MATLAB$^\circledR$ implementation of a classical method and sampling of designed uniform distributions, the experiments demonstrate that the embedding significantly reduces run-time from $\mathbf{5.23 \times 10^3}$ s of traditional discretization method to $\mathbf{0.224}$ s, with high accuracies (average F-measure is $\mathbf{0.9665}$ with batch gradient descent and resilient backpropagation).
Tasks
Published	2018-04-24
URL	http://arxiv.org/abs/1804.08951v2
PDF	http://arxiv.org/pdf/1804.08951v2.pdf
PWC	https://paperswithcode.com/paper/deep-neural-network-based-subspace-learning
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Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search


Title	Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search
Authors	Xinye Cai, Haoran Sun, Chunyang Zhu, Zhenyu Li, Qingfu Zhang
Abstract	In this paper, an evolutionary many-objective optimization algorithm based on corner solution search (MaOEA-CS) was proposed. MaOEA-CS implicitly contains two phases: the exploitative search for the most important boundary optimal solutions - corner solutions, at the first phase, and the use of angle-based selection [1] with the explorative search for the extension of PF approximation at the second phase. Due to its high efficiency and robustness to the shapes of PFs, it has won the CEC’2017 Competition on Evolutionary Many-Objective Optimization. In addition, MaOEA-CS has also been applied on two real-world engineering optimization problems with very irregular PFs. The experimental results show that MaOEA-CS outperforms other six state-of-the-art compared algorithms, which indicates it has the ability to handle real-world complex optimization problems with irregular PFs.
Tasks
Published	2018-06-08
URL	http://arxiv.org/abs/1806.02967v1
PDF	http://arxiv.org/pdf/1806.02967v1.pdf
PWC	https://paperswithcode.com/paper/locating-the-boundaries-of-pareto-fronts-a
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Molecular Hypergraph Grammar with its Application to Molecular Optimization


Title	Molecular Hypergraph Grammar with its Application to Molecular Optimization
Authors	Hiroshi Kajino
Abstract	Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a molecule into/from its latent continuous vector, and BO optimizes a latent continuous vector (and its corresponding molecule) within a limited number of property evaluations. While the most recent work, for the first time, achieved 100% validity, its architecture is rather complex due to auxiliary neural networks other than VAE, making it difficult to train. This paper presents a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to achieve 100% validity. Our idea is to develop a graph grammar encoding the hard chemical constraints, called molecular hypergraph grammar (MHG), which guides VAE to always generate valid molecules. We also present an algorithm to construct MHG from a set of molecules.
Tasks
Published	2018-09-08
URL	http://arxiv.org/abs/1809.02745v2
PDF	http://arxiv.org/pdf/1809.02745v2.pdf
PWC	https://paperswithcode.com/paper/molecular-hypergraph-grammar-with-its
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Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition


Title	Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition
Authors	David B. Ramsay, Kevin Kilgour, Dominik Roblek, Matthew Sharifi
Abstract	Low power digital signal processors (DSPs) typically have a very limited amount of memory in which to cache data. In this paper we develop efficient bottleneck feature (BNF) extractors that can be run on a DSP, and retrain a baseline large-vocabulary continuous speech recognition (LVCSR) system to use these BNFs with only a minimal loss of accuracy. The small BNFs allow the DSP chip to cache more audio features while the main application processor is suspended, thereby reducing the overall battery usage. Our presented system is able to reduce the footprint of standard, fixed point DSP spectral features by a factor of 10 without any loss in word error rate (WER) and by a factor of 64 with only a 5.8% relative increase in WER.
Tasks	Large Vocabulary Continuous Speech Recognition, Speech Recognition
Published	2018-10-31
URL	http://arxiv.org/abs/1811.00006v1
PDF	http://arxiv.org/pdf/1811.00006v1.pdf
PWC	https://paperswithcode.com/paper/low-dimensional-bottleneck-features-for-on
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TAPAS: Train-less Accuracy Predictor for Architecture Search


Title	TAPAS: Train-less Accuracy Predictor for Architecture Search
Authors	R. Istrate, F. Scheidegger, G. Mariani, D. Nikolopoulos, C. Bekas, A. C. I. Malossi
Abstract	In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated high-performance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources.
Tasks	Neural Architecture Search
Published	2018-06-01
URL	http://arxiv.org/abs/1806.00250v1
PDF	http://arxiv.org/pdf/1806.00250v1.pdf
PWC	https://paperswithcode.com/paper/tapas-train-less-accuracy-predictor-for
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Regular omega-Languages with an Informative Right Congruence


Title	Regular omega-Languages with an Informative Right Congruence
Authors	Dana Angluin, Dana Fisman
Abstract	A regular language is almost fully characterized by its right congruence relation. Indeed, a regular language can always be recognized by a DFA isomorphic to the automaton corresponding to its right congruence, henceforth the Rightcon automaton. The same does not hold for regular omega-languages. The right congruence of a regular omega-language is not informative enough; many regular omega-languages have a trivial right congruence, and in general it is not always possible to define an omega-automaton recognizing a given language that is isomorphic to the rightcon automaton. The class of weak regular omega-languages does have an informative right congruence. That is, any weak regular omega-language can always be recognized by a deterministic B"uchi automaton that is isomorphic to the rightcon automaton. Weak regular omega-languages reside in the lower levels of the expressiveness hierarchy of regular omega-languages. Are there more expressive sub-classes of regular omega languages that have an informative right congruence? Can we fully characterize the class of languages with a trivial right congruence? In this paper we try to place some additional pieces of this big puzzle.
Tasks
Published	2018-09-10
URL	http://arxiv.org/abs/1809.03108v1
PDF	http://arxiv.org/pdf/1809.03108v1.pdf
PWC	https://paperswithcode.com/paper/regular-omega-languages-with-an-informative
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STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection


Title	STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection
Authors	Sangmin Lee, Hak Gu Kim, Yong Man Ro
Abstract	In this paper, we propose a novel abnormal event detection method with spatio-temporal adversarial networks (STAN). We devise a spatio-temporal generator which synthesizes an inter-frame by considering spatio-temporal characteristics with bidirectional ConvLSTM. A proposed spatio-temporal discriminator determines whether an input sequence is real-normal or not with 3D convolutional layers. These two networks are trained in an adversarial way to effectively encode spatio-temporal features of normal patterns. After the learning, the generator and the discriminator can be independently used as detectors, and deviations from the learned normal patterns are detected as abnormalities. Experimental results show that the proposed method achieved competitive performance compared to the state-of-the-art methods. Further, for the interpretation, we visualize the location of abnormal events detected by the proposed networks using a generator loss and discriminator gradients.
Tasks
Published	2018-04-23
URL	http://arxiv.org/abs/1804.08381v1
PDF	http://arxiv.org/pdf/1804.08381v1.pdf
PWC	https://paperswithcode.com/paper/stan-spatio-temporal-adversarial-networks-for
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Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers


Title	Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers
Authors	Sherif Abdulatif, Fady Aziz, Karim Armanious, Bernhard Kleiner, Bin Yang, Urs Schneider
Abstract	Obtaining a smart surveillance requires a sensing system that can capture accurate and detailed information for the human walking style. The radar micro-Doppler ($\boldsymbol{\mu}$-D) analysis is proved to be a reliable metric for studying human locomotions. Thus, $\boldsymbol{\mu}$-D signatures can be used to identify humans based on their walking styles. Additionally, the signatures contain information about the radar cross section (RCS) of the moving subject. This paper investigates the effect of human body characteristics on human identification based on their $\boldsymbol{\mu}$-D signatures. In our proposed experimental setup, a treadmill is used to collect $\boldsymbol{\mu}$-D signatures of 22 subjects with different genders and body characteristics. Convolutional autoencoders (CAE) are then used to extract the latent space representation from the $\boldsymbol{\mu}$-D signatures. It is then interpreted in two dimensions using t-distributed stochastic neighbor embedding (t-SNE). Our study shows that the body mass index (BMI) has a correlation with the $\boldsymbol{\mu}$-D signature of the walking subject. A 50-layer deep residual network is then trained to identify the walking subject based on the $\boldsymbol{\mu}$-D signature. We achieve an accuracy of 98% on the test set with high signal-to-noise-ratio (SNR) and 84% in case of different SNR levels.
Tasks	Person Identification
Published	2018-11-17
URL	http://arxiv.org/abs/1811.07173v2
PDF	http://arxiv.org/pdf/1811.07173v2.pdf
PWC	https://paperswithcode.com/paper/person-identification-and-body-mass-index-a
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OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google’s Knowledge Graph


Title	OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google’s Knowledge Graph
Authors	Niel Chah
Abstract	This paper reconstructs the Freebase data dumps to understand the underlying ontology behind Google’s semantic search feature. The Freebase knowledge base was a major Semantic Web and linked data technology that was acquired by Google in 2010 to support the Google Knowledge Graph, the backend for Google search results that include structured answers to queries instead of a series of links to external resources. After its shutdown in 2016, Freebase is contained in a data dump of 1.9 billion Resource Description Format (RDF) triples. A recomposition of the Freebase ontology will be analyzed in relation to concepts and insights from the literature on classification by Bowker and Star. This paper will explore how the Freebase ontology is shaped by many of the forces that also shape classification systems through a deep dive into the ontology and a small correlational study. These findings will provide a glimpse into the proprietary blackbox Knowledge Graph and what is meant by Google’s mission to “organize the world’s information and make it universally accessible and useful”.
Tasks
Published	2018-05-10
URL	http://arxiv.org/abs/1805.03885v2
PDF	http://arxiv.org/pdf/1805.03885v2.pdf
PWC	https://paperswithcode.com/paper/ok-google-what-is-your-ontology-or-exploring
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U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification


Title	U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification
Authors	Ty Nguyen, Tolga Ozaslan, Ian D. Miller, James Keller, Giuseppe Loianno, Camillo J. Taylor, Daniel D. Lee, Vijay Kumar, Joseph H. Harwood, Jennifer Wozencraft
Abstract	Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures. Conventional manual inspection methods require inspectors to climb along a penstock to spot corrosion, rust and crack formation which is unsafe, labor-intensive, and requires intensive training. This work presents an alternative approach using a Micro Aerial Vehicle (MAV) that autonomously flies to collect imagery which is then fed into a pretrained deep-learning model to identify corrosion. Our simplified U-Net trained with less than 40 image samples can do inference at 12 fps on a single GPU. We analyze different loss functions to solve the class imbalance problem, followed by a discussion on choosing proper metrics and weights for object classes. Results obtained with the dataset collected from Center Hill Dam, TN show that focal loss function, combined with a proper set of class weights yield better segmentation results than the base loss, Softmax cross entropy. Our method can be used in combination with planning algorithm to offer a complete, safe and cost-efficient solution to autonomous infrastructure inspection.
Tasks
Published	2018-09-18
URL	http://arxiv.org/abs/1809.06576v1
PDF	http://arxiv.org/pdf/1809.06576v1.pdf
PWC	https://paperswithcode.com/paper/u-net-for-mav-based-penstock-inspection-an
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Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models


Title	Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models
Authors	Daniel Watson, Nasser Zalmout, Nizar Habash
Abstract	Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the complexity of the language increase the difficulty of the problem. To address these challenges, we use a sequence-to-sequence model with character-based attention, which in addition to its self-learned character embeddings, uses word embeddings pre-trained with an approach that also models subword information. This provides the neural model with access to more linguistic information especially suitable for text normalization, without large parallel corpora. We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.
Tasks	Word Embeddings
Published	2018-09-05
URL	http://arxiv.org/abs/1809.01534v1
PDF	http://arxiv.org/pdf/1809.01534v1.pdf
PWC	https://paperswithcode.com/paper/utilizing-character-and-word-embeddings-for
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Document Structure Measure for Hypernym discovery


Title	Document Structure Measure for Hypernym discovery
Authors	Aswin Kannan, Shanmukha C Guttula, Balaji Ganesan, Hima P Karanam, Arun Kumar
Abstract	Hypernym discovery is the problem of finding terms that have is-a relationship with a given term. We introduce a new context type, and a relatedness measure to differentiate hypernyms from other types of semantic relationships. Our Document Structure measure is based on hierarchical position of terms in a document, and their presence or otherwise in definition text. This measure quantifies the document structure using multiple attributes, and classes of weighted distance functions.
Tasks	Hypernym Discovery
Published	2018-11-30
URL	http://arxiv.org/abs/1811.12728v1
PDF	http://arxiv.org/pdf/1811.12728v1.pdf
PWC	https://paperswithcode.com/paper/document-structure-measure-for-hypernym
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Improved Search Strategies for Determining Facial Expression


Title	Improved Search Strategies for Determining Facial Expression
Authors	Michael Bao, Xinru Hua, Ronald Fedkiw
Abstract	It is well known that popular optimization techniques can lead to overfitting or even a lack of convergence altogether; thus, practitioners often utilize ad hoc regularization terms added to the energy functional. When carefully crafted, these regularizations can produce compelling results. However, regularization changes both the energy landscape and the solution to the optimization problem, which can result in underfitting. Surprisingly, many practitioners both add regularization and claim that their model lacks the expressivity to fit the data. Motivated by a geometric interpretation of the linearized search space, we propose an approach that ameliorates overfitting without the need for regularization terms that restrict the expressiveness of the underlying model. We illustrate the efficacy of our approach on minimization problems related to three-dimensional facial expression estimation where overfitting clouds semantic understanding and regularization may lead to underfitting that misses or misinterprets subtle expressions.
Tasks
Published	2018-12-07
URL	http://arxiv.org/abs/1812.02897v1
PDF	http://arxiv.org/pdf/1812.02897v1.pdf
PWC	https://paperswithcode.com/paper/improved-search-strategies-for-determining
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