Paper Group ANR 687
Feedforward Neural Network for Time Series Anomaly Detection. A randomized gradient-free attack on ReLU networks. A General Family of Robust Stochastic Operators for Reinforcement Learning. Action Quality Assessment Across Multiple Actions. A Learning-Based Framework for Line-Spectra Super-resolution. Answering Hindsight Queries with Lifted Dynamic …
Feedforward Neural Network for Time Series Anomaly Detection
| Title | Feedforward Neural Network for Time Series Anomaly Detection |
| Authors | Zhang Rong, Dong Shandong, Nie Xin, Xiao Shiguang |
| Abstract | Time series anomaly detection is usually formulated as finding outlier data points relative to some usual data, which is also an important problem in industry and academia. To ensure systems working stably, internet companies, banks and other companies need to monitor time series, which is called KPI (Key Performance Indicators), such as CPU used, number of orders, number of online users and so on. However, millions of time series have several shapes (e.g. seasonal KPIs, KPIs of timed tasks and KPIs of CPU used), so that it is very difficult to use a simple statistical model to detect anomaly for all kinds of time series. Although some anomaly detectors have developed many years and some supervised models are also available in this field, we find many methods have their own disadvantages. In this paper, we present our system, which is based on deep feedforward neural network and detect anomaly points of time series. The main difference between our system and other systems based on supervised models is that we do not need feature engineering of time series to train deep feedforward neural network in our system, which is essentially an end-to-end system. |
| Tasks | Anomaly Detection, Feature Engineering, Time Series |
| Published | 2018-12-20 |
| URL | http://arxiv.org/abs/1812.08389v2 |
| http://arxiv.org/pdf/1812.08389v2.pdf | |
| PWC | https://paperswithcode.com/paper/feedforward-neural-network-for-time-series |
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A randomized gradient-free attack on ReLU networks
| Title | A randomized gradient-free attack on ReLU networks |
| Authors | Francesco Croce, Matthias Hein |
| Abstract | It has recently been shown that neural networks but also other classifiers are vulnerable to so called adversarial attacks e.g. in object recognition an almost non-perceivable change of the image changes the decision of the classifier. Relatively fast heuristics have been proposed to produce these adversarial inputs but the problem of finding the optimal adversarial input, that is with the minimal change of the input, is NP-hard. While methods based on mixed-integer optimization which find the optimal adversarial input have been developed, they do not scale to large networks. Currently, the attack scheme proposed by Carlini and Wagner is considered to produce the best adversarial inputs. In this paper we propose a new attack scheme for the class of ReLU networks based on a direct optimization on the resulting linear regions. In our experimental validation we improve in all except one experiment out of 18 over the Carlini-Wagner attack with a relative improvement of up to 9%. As our approach is based on the geometrical structure of ReLU networks, it is less susceptible to defences targeting their functional properties. |
| Tasks | Object Recognition |
| Published | 2018-11-28 |
| URL | http://arxiv.org/abs/1811.11493v1 |
| http://arxiv.org/pdf/1811.11493v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-randomized-gradient-free-attack-on-relu |
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A General Family of Robust Stochastic Operators for Reinforcement Learning
| Title | A General Family of Robust Stochastic Operators for Reinforcement Learning |
| Authors | Yingdong Lu, Mark S. Squillante, Chai Wah Wu |
| Abstract | We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors. Various theoretical results are established, which include showing on a sample path basis that our family of operators preserve optimality and increase the action gap. Our empirical results illustrate the strong benefits of our family of operators, significantly outperforming the classical Bellman operator and recently proposed operators. |
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| Published | 2018-05-21 |
| URL | https://arxiv.org/abs/1805.08122v2 |
| https://arxiv.org/pdf/1805.08122v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-general-family-of-robust-stochastic |
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Action Quality Assessment Across Multiple Actions
| Title | Action Quality Assessment Across Multiple Actions |
| Authors | Paritosh Parmar, Brendan Tran Morris |
| Abstract | Can learning to measure the quality of an action help in measuring the quality of other actions? If so, can consolidated samples from multiple actions help improve the performance of current approaches? In this paper, we carry out experiments to see if knowledge transfer is possible in the action quality assessment (AQA) setting. Experiments are carried out on our newly released AQA dataset (http://rtis.oit.unlv.edu/datasets.html) consisting of 1106 action samples from seven actions with quality scores as measured by expert human judges. Our experimental results show that there is utility in learning a single model across multiple actions. |
| Tasks | Transfer Learning |
| Published | 2018-12-15 |
| URL | http://arxiv.org/abs/1812.06367v2 |
| http://arxiv.org/pdf/1812.06367v2.pdf | |
| PWC | https://paperswithcode.com/paper/action-quality-assessment-across-multiple |
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A Learning-Based Framework for Line-Spectra Super-resolution
| Title | A Learning-Based Framework for Line-Spectra Super-resolution |
| Authors | Gautier Izacard, Brett Bernstein, Carlos Fernandez-Granda |
| Abstract | We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires modifying the training data accordingly. Numerical experiments show that the approach performs competitively with classical methods designed for additive Gaussian noise at a range of noise levels, and is also effective in the presence of impulsive noise. |
| Tasks | Super-Resolution |
| Published | 2018-11-14 |
| URL | https://arxiv.org/abs/1811.05844v2 |
| https://arxiv.org/pdf/1811.05844v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-learning-based-framework-for-line-spectra |
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Answering Hindsight Queries with Lifted Dynamic Junction Trees
| Title | Answering Hindsight Queries with Lifted Dynamic Junction Trees |
| Authors | Marcel Gehrke, Tanya Braun, Ralf Möller |
| Abstract | The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. We extend LDJT to (i) solve the smoothing inference problem to answer hindsight queries by introducing an efficient backward pass and (ii) discuss different options to instantiate a first-order cluster representation during a backward pass. Further, our relational forward backward algorithm makes hindsight queries to the very beginning feasible. LDJT answers multiple temporal queries faster than the static lifted junction tree algorithm on an unrolled model, which performs smoothing during message passing. |
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| Published | 2018-07-02 |
| URL | http://arxiv.org/abs/1807.01586v1 |
| http://arxiv.org/pdf/1807.01586v1.pdf | |
| PWC | https://paperswithcode.com/paper/answering-hindsight-queries-with-lifted |
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Soft Concept Analysis
| Title | Soft Concept Analysis |
| Authors | Robert E. Kent |
| Abstract | In this chapter we discuss soft concept analysis, a study which identifies an enriched notion of “conceptual scale” as developed in formal concept analysis with an enriched notion of “linguistic variable” as discussed in fuzzy logic. The identification “enriched conceptual scale” = “enriched linguistic variable” was made in a previous paper (Enriched interpretation, Robert E. Kent). In this chapter we offer further arguments for the importance of this identification by discussing the philosophy, spirit, and practical application of conceptual scaling to the discovery, conceptual analysis, interpretation, and categorization of networked information resources. We argue that a linguistic variable, which has been defined at just the right generalization of valuated categories, provides a natural definition for the process of soft conceptual scaling. This enrichment using valuated categories models the relation of indiscernability, a notion of central importance in rough set theory. At a more fundamental level for soft concept analysis, it also models the derivation of formal concepts, a process of central importance in formal concept analysis. Soft concept analysis is synonymous with enriched concept analysis. From one viewpoint, the study of soft concept analysis that is initiated here extends formal concept analysis to soft computational structures. From another viewpoint, soft concept analysis provides a natural foundation for soft computation by unifying and explaining notions from soft computation in terms of suitably generalized notions from formal concept analysis, rough set theory and fuzzy set theory. |
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| Published | 2018-10-21 |
| URL | http://arxiv.org/abs/1810.09036v1 |
| http://arxiv.org/pdf/1810.09036v1.pdf | |
| PWC | https://paperswithcode.com/paper/soft-concept-analysis |
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Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
| Title | Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data |
| Authors | Aayush Prakash, Shaad Boochoon, Mark Brophy, David Acuna, Eric Cameracci, Gavriel State, Omer Shapira, Stan Birchfield |
| Abstract | We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure and context of the scene. In contrast to DR, which places objects and distractors randomly according to a uniform probability distribution, SDR places objects and distractors randomly according to probability distributions that arise from the specific problem at hand. In this manner, SDR-generated imagery enables the neural network to take the context around an object into consideration during detection. We demonstrate the power of SDR for the problem of 2D bounding box car detection, achieving competitive results on real data after training only on synthetic data. On the KITTI easy, moderate, and hard tasks, we show that SDR outperforms other approaches to generating synthetic data (VKITTI, Sim 200k, or DR), as well as real data collected in a different domain (BDD100K). Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone. |
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| Published | 2018-10-23 |
| URL | http://arxiv.org/abs/1810.10093v1 |
| http://arxiv.org/pdf/1810.10093v1.pdf | |
| PWC | https://paperswithcode.com/paper/structured-domain-randomization-bridging-the |
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Natural Language Generation for Electronic Health Records
| Title | Natural Language Generation for Electronic Health Records |
| Authors | Scott Lee |
| Abstract | A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that features in many contemporary machine translation systems, to generate synthetic chief complaints from discrete variables in EHRs, like age group, gender, and discharge diagnosis. After being trained end-to-end on authentic records, the model can generate realistic chief complaint text that preserves much of the epidemiological information in the original data. As a side effect of the model’s optimization goal, these synthetic chief complaints are also free of relatively uncommon abbreviation and misspellings, and they include none of the personally-identifiable information (PII) that was in the training data, suggesting it may be used to support the de-identification of text in EHRs. When combined with algorithms like generative adversarial networks (GANs), our model could be used to generate fully-synthetic EHRs, facilitating data sharing between healthcare providers and researchers and improving our ability to develop machine learning methods tailored to the information in healthcare data. |
| Tasks | Machine Translation, Text Generation |
| Published | 2018-06-01 |
| URL | http://arxiv.org/abs/1806.01353v1 |
| http://arxiv.org/pdf/1806.01353v1.pdf | |
| PWC | https://paperswithcode.com/paper/natural-language-generation-for-electronic |
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Applying Distributional Compositional Categorical Models of Meaning to Language Translation
| Title | Applying Distributional Compositional Categorical Models of Meaning to Language Translation |
| Authors | Brian Tyrrell |
| Abstract | The aim of this paper is twofold: first we will use vector space distributional compositional categorical models of meaning to compare the meaning of sentences in Irish and in English (and thus ascertain when a sentence is the translation of another sentence) using the cosine similarity score. Then we shall outline a procedure which translates nouns by understanding their context, using a conceptual space model of cognition. We shall use metrics on the category ConvexRel to determine the distance between concepts (and determine when a noun is the translation of another noun). This paper will focus on applications to Irish, a member of the Gaelic family of languages. |
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| Published | 2018-11-08 |
| URL | http://arxiv.org/abs/1811.03274v1 |
| http://arxiv.org/pdf/1811.03274v1.pdf | |
| PWC | https://paperswithcode.com/paper/applying-distributional-compositional |
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Specificity measures and reference
| Title | Specificity measures and reference |
| Authors | Albert Gatt, Nicolás Marín, Gustavo Rivas-Gervilla, Daniel Sánchez |
| Abstract | In this paper we study empirically the validity of measures of referential success for referring expressions involving gradual properties. More specifically, we study the ability of several measures of referential success to predict the success of a user in choosing the right object, given a referring expression. Experimental results indicate that certain fuzzy measures of success are able to predict human accuracy in reference resolution. Such measures are therefore suitable for the estimation of the success or otherwise of a referring expression produced by a generation algorithm, especially in case the properties in a domain cannot be assumed to have crisp denotations. |
| Tasks | |
| Published | 2018-09-30 |
| URL | http://arxiv.org/abs/1810.00333v1 |
| http://arxiv.org/pdf/1810.00333v1.pdf | |
| PWC | https://paperswithcode.com/paper/specificity-measures-and-reference |
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Seeing Colors: Learning Semantic Text Encoding for Classification
| Title | Seeing Colors: Learning Semantic Text Encoding for Classification |
| Authors | Shah Nawaz, Alessandro Calefati, Muhammad Kamran Janjua, Ignazio Gallo |
| Abstract | The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents? To answer this question we present a novel text classification method which converts a text document into an encoded image, using word embedding and capabilities of Convolutional Neural Networks (CNNs), successfully employed in image classification. We evaluate our approach by obtaining promising results on some well-known benchmark datasets for text classification. This work allows the application of many of the advanced CNN architectures developed for Computer Vision to Natural Language Processing. We test the proposed approach on a multi-modal dataset, proving that it is possible to use a single deep model to represent text and image in the same feature space. |
| Tasks | Image Classification, Text Classification |
| Published | 2018-08-31 |
| URL | http://arxiv.org/abs/1808.10822v1 |
| http://arxiv.org/pdf/1808.10822v1.pdf | |
| PWC | https://paperswithcode.com/paper/seeing-colors-learning-semantic-text-encoding |
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How clever is the FiLM model, and how clever can it be?
| Title | How clever is the FiLM model, and how clever can it be? |
| Authors | Alexander Kuhnle, Huiyuan Xie, Ann Copestake |
| Abstract | The FiLM model achieves close-to-perfect performance on the diagnostic CLEVR dataset and is distinguished from other such models by having a comparatively simple and easily transferable architecture. In this paper, we investigate in more detail the ability of FiLM to learn various linguistic constructions. Our main results show that (a) FiLM is not able to learn relational statements straight away except for very simple instances, (b) training on a broader set of instances as well as pretraining on simpler instance types can help alleviate these learning difficulties, (c) mixing is less robust than pretraining and very sensitive to the compositional structure of the dataset. Overall, our results suggest that the approach of big all-encompassing datasets and the paradigm of “the effectiveness of data” may have fundamental limitations. |
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| Published | 2018-09-09 |
| URL | http://arxiv.org/abs/1809.03044v1 |
| http://arxiv.org/pdf/1809.03044v1.pdf | |
| PWC | https://paperswithcode.com/paper/how-clever-is-the-film-model-and-how-clever |
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Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell
| Title | Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell |
| Authors | Pia Sommerauer, Antske Fokkens |
| Abstract | This paper presents an approach for investigating the nature of semantic information captured by word embeddings. We propose a method that extends an existing human-elicited semantic property dataset with gold negative examples using crowd judgments. Our experimental approach tests the ability of supervised classifiers to identify semantic features in word embedding vectors and com- pares this to a feature-identification method based on full vector cosine similarity. The idea behind this method is that properties identified by classifiers, but not through full vector comparison are captured by embeddings. Properties that cannot be identified by either method are not. Our results provide an initial indication that semantic properties relevant for the way entities interact (e.g. dangerous) are captured, while perceptual information (e.g. colors) is not represented. We conclude that, though preliminary, these results show that our method is suitable for identifying which properties are captured by embeddings. |
| Tasks | Word Embeddings |
| Published | 2018-09-05 |
| URL | http://arxiv.org/abs/1809.01375v1 |
| http://arxiv.org/pdf/1809.01375v1.pdf | |
| PWC | https://paperswithcode.com/paper/firearms-and-tigers-are-dangerous-kitchen |
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Peeking the Impact of Points of Interests on Didi
| Title | Peeking the Impact of Points of Interests on Didi |
| Authors | Yonghong Tian, Zeyu Li, Zhiwei Xu, Xuying Meng, Bing Zheng |
| Abstract | Recently, the online car-hailing service, Didi, has emerged as a leader in the sharing economy. Used by passengers and drivers extensive, it becomes increasingly important for the car-hailing service providers to minimize the waiting time of passengers and optimize the vehicle utilization, thus to improve the overall user experience. Therefore, the supply-demand estimation is an indispensable ingredient of an efficient online car-hailing service. To improve the accuracy of the estimation results, we analyze the implicit relationships between the points of Interest (POI) and the supply-demand gap in this paper. The different categories of POIs have positive or negative effects on the estimation, we propose a POI selection scheme and incorporate it into XGBoost [1] to achieve more accurate estimation results. Our experiment demonstrates our method provides more accurate estimation results and more stable estimation results than the existing methods. |
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| Published | 2018-04-06 |
| URL | http://arxiv.org/abs/1804.04176v1 |
| http://arxiv.org/pdf/1804.04176v1.pdf | |
| PWC | https://paperswithcode.com/paper/peeking-the-impact-of-points-of-interests-on |
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