Paper Group NANR 86
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks. Differentially Private Robust Low-Rank Approximation. Understanding the Effect of Gender and Stance in Opinion Expression in Debates on ``Abortion’'. AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos. Universal Dependencies and Quantitative Typolo …
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks
| Title | Denoising Time Series Data Using Asymmetric Generative Adversarial Networks |
| Authors | Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, David Hairston |
| Abstract | Denoising data is a preprocessing step for several time series mining algorithms. This step is especially important if the noise in data originates from diverse sources. Consequently, it is commonly used in biomedical applications that use Electroencephalography (EEG) data. In EEG data noise can occur due to ocular, muscular and cardiac activities. In this paper, we explicitly learn to remove noise from time series data without assuming a prior distribution of noise. We propose an online, fully automated, end-to-end system for denoising time series data. Our model for denoising time series is trained using unpaired training corpora and does not need information about the source of the noise or how it is manifested in the time series. We propose a new architecture called AsymmetricGAN that uses a generative adversarial network for denoising time series data. To analyze our approach, we create a synthetic dataset that is easy to visualize and interpret. We also evaluate and show the effectiveness of our approach on an existing EEG dataset. |
| Tasks | Denoising, EEG, EEG Denoising, Time Series |
| Published | 2018-06-17 |
| URL | https://doi.org/10.1007/978-3-319-93040-4_23 |
| https://www.researchgate.net/publication/325808737_Denoising_Time_Series_Data_Using_Asymmetric_Generative_Adversarial_Networks | |
| PWC | https://paperswithcode.com/paper/denoising-time-series-data-using-asymmetric |
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Differentially Private Robust Low-Rank Approximation
| Title | Differentially Private Robust Low-Rank Approximation |
| Authors | Raman Arora, Vladimir Braverman, Jalaj Upadhyay |
| Abstract | In this paper, we study the following robust low-rank matrix approximation problem: given a matrix $A \in \R^{n \times d}$, find a rank-$k$ matrix $B$, while satisfying differential privacy, such that $ \norm{ A - B }_p \leq \alpha \mathsf{OPT}_k(A) + \tau,$ where $\norm{ M }_p$ is the entry-wise $\ell_p$-norm and $\mathsf{OPT}k(A):=\min{\mathsf{rank}(X) \leq k} \norm{ A - X}_p$. It is well known that low-rank approximation w.r.t. entrywise $\ell_p$-norm, for $p \in [1,2)$, yields robustness to gross outliers in the data. We propose an algorithm that guarantees $\alpha=\widetilde{O}(k^2), \tau=\widetilde{O}(k^2(n+kd)/\varepsilon)$, runs in $\widetilde O((n+d)\poly~k)$ time and uses $O(k(n+d)\log k)$ space. We study extensions to the streaming setting where entries of the matrix arrive in an arbitrary order and output is produced at the very end or continually. We also study the related problem of differentially private robust principal component analysis (PCA), wherein we return a rank-$k$ projection matrix $\Pi$ such that $\norm{ A - A \Pi }_p \leq \alpha \mathsf{OPT}_k(A) + \tau.$ |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7668-differentially-private-robust-low-rank-approximation |
| http://papers.nips.cc/paper/7668-differentially-private-robust-low-rank-approximation.pdf | |
| PWC | https://paperswithcode.com/paper/differentially-private-robust-low-rank |
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Understanding the Effect of Gender and Stance in Opinion Expression in Debates on ``Abortion’’
| Title | Understanding the Effect of Gender and Stance in Opinion Expression in Debates on ``Abortion’’ | |
| Authors | Esin Durmus, Claire Cardie |
| Abstract | In this paper, we focus on understanding linguistic differences across groups with different self-identified gender and stance in expressing opinions about ABORTION. We provide a new dataset consisting of users{'} gender, stance on ABORTION as well as the debates in ABORTION drawn from debate.org. We use the gender and stance information to identify significant linguistic differences across individuals with different gender and stance. We show the importance of considering the stance information along with the gender since we observe significant linguistic differences across individuals with different stance even within the same gender group. |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-1110/ |
| https://www.aclweb.org/anthology/W18-1110 | |
| PWC | https://paperswithcode.com/paper/understanding-the-effect-of-gender-and-stance |
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AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos
| Title | AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos |
| Authors | Zheng Shou, Hang Gao, Lei Zhang, Kazuyuki Miyazawa, Shih-Fu Chang |
| Abstract | Temporal Action Localization (TAL) in untrimmed video is important for many applications. But it is very expensive to annotate the segment-level ground truth (action class and temporal boundary). This raises the interest of addressing TAL with weak supervision, namely only video-level annotations are available during training). However, the state-of-the-art weakly-supervised TAL methods only focus on generating good Class Activation Sequence (CAS) over time but conduct simple thresholding on CAS to localize actions. In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance. We propose a novel Outer-Inner-Contrastive (OIC) loss to automatically discover the needed segment-level supervision for training such a boundary predictor. Our method achieves dramatically improved performance: under the IoU threshold 0.5, our method improves mAP on THUMOS’14 from 13.7% to 21.2% and mAP on ActivityNet from 7.4% to 27.3%. It is also very encouraging to see that our weakly-supervised method achieves comparable results with some fully-supervised methods. |
| Tasks | Action Localization, Temporal Action Localization, Weakly Supervised Action Localization, Weakly-supervised Temporal Action Localization |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Zheng_Shou_AutoLoc_Weakly-supervised_Temporal_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Zheng_Shou_AutoLoc_Weakly-supervised_Temporal_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/autoloc-weakly-supervised-temporal-action-1 |
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Universal Dependencies and Quantitative Typological Trends. A Case Study on Word Order
| Title | Universal Dependencies and Quantitative Typological Trends. A Case Study on Word Order |
| Authors | Chiara Alzetta, Felice Dell{'}Orletta, Simonetta Montemagni, Giulia Venturi |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1719/ |
| https://www.aclweb.org/anthology/L18-1719 | |
| PWC | https://paperswithcode.com/paper/universal-dependencies-and-quantitative |
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Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Objectives
| Title | Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Objectives |
| Authors | Song Zhou, Swati Gupta, Madeleine Udell |
| Abstract | The original simplicial method (OSM), a variant of the classic Kelley’s cutting plane method, has been shown to converge to the minimizer of a composite convex and submodular objective, though no rate of convergence for this method was known. Moreover, OSM is required to solve subproblems in each iteration whose size grows linearly in the number of iterations. We propose a limited memory version of Kelley’s method (L-KM) and of OSM that requires limited memory (at most n+ 1 constraints for an n-dimensional problem) independent of the iteration. We prove convergence for L-KM when the convex part of the objective g is strongly convex and show it converges linearly when g is also smooth. Our analysis relies on duality between minimization of the composite convex and submodular objective and minimization of a convex function over the submodular base polytope. We introduce a limited memory version, L-FCFW, of the Fully-Corrective Frank-Wolfe (FCFW) method with approximate correction, to solve the dual problem. We show that L-FCFW and L-KM are dual algorithms that produce the same sequence of iterates; hence both converge linearly (when g is smooth and strongly convex) and with limited memory. We propose L-KM to minimize composite convex and submodular objectives; however, our results on L-FCFW hold for general polytopes and may be of independent interest. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7694-limited-memory-kelleys-method-converges-for-composite-convex-and-submodular-objectives |
| http://papers.nips.cc/paper/7694-limited-memory-kelleys-method-converges-for-composite-convex-and-submodular-objectives.pdf | |
| PWC | https://paperswithcode.com/paper/limited-memory-kelleys-method-converges-for |
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Simple Neologism Based Domain Independent Models to Predict Year of Authorship
| Title | Simple Neologism Based Domain Independent Models to Predict Year of Authorship |
| Authors | Vivek Kulkarni, Yingtao Tian, D, Parth iwala, Steve Skiena |
| Abstract | We present domain independent models to date documents based only on neologism usage patterns. Our models capture patterns of neologism usage over time to date texts, provide insights into temporal locality of word usage over a span of 150 years, and generalize to various domains like News, Fiction, and Non-Fiction with competitive performance. Quite intriguingly, we show that by modeling only the distribution of usage counts over neologisms (the model being agnostic of the particular words themselves), we achieve competitive performance using several orders of magnitude fewer features (only 200 input features) compared to state of the art models some of which use 200K features. |
| Tasks | Domain Adaptation, Information Retrieval |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1017/ |
| https://www.aclweb.org/anthology/C18-1017 | |
| PWC | https://paperswithcode.com/paper/simple-neologism-based-domain-independent |
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Inverse Composition Discriminative Optimization for Point Cloud Registration
| Title | Inverse Composition Discriminative Optimization for Point Cloud Registration |
| Authors | Jayakorn Vongkulbhisal, Beñat Irastorza Ugalde, Fernando De la Torre, João P. Costeira |
| Abstract | Rigid Point Cloud Registration (PCReg) refers to the problem of finding the rigid transformation between two sets of point clouds. This problem is particularly important due to the advances in new 3D sensing hardware, and it is challenging because neither the correspondence nor the transformation parameters are known. Traditional local PCReg methods (e.g., ICP) rely on local optimization algorithms, which can get trapped in bad local minima in the presence of noise, outliers, bad initializations, etc. To alleviate these issues, this paper proposes Inverse Composition Discriminative Optimization (ICDO), an extension of Discriminative Optimization (DO), which learns a sequence of update steps from synthetic training data that search the parameter space for an improved solution. Unlike DO, ICDO is object-independent and generalizes even to unseen shapes. We evaluated ICDO on both synthetic and real data, and show that ICDO can match the speed and outperform the accuracy of state-of-the-art PCReg algorithms. |
| Tasks | Point Cloud Registration |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Vongkulbhisal_Inverse_Composition_Discriminative_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Vongkulbhisal_Inverse_Composition_Discriminative_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/inverse-composition-discriminative |
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The TALP-UPC Machine Translation Systems for WMT18 News Shared Translation Task
| Title | The TALP-UPC Machine Translation Systems for WMT18 News Shared Translation Task |
| Authors | Noe Casas, Carlos Escolano, Marta R. Costa-juss{`a}, Jos{'e} A. R. Fonollosa |
| Abstract | In this article we describe the TALP-UPC research group participation in the WMT18 news shared translation task for Finnish-English and Estonian-English within the multi-lingual subtrack. All of our primary submissions implement an attention-based Neural Machine Translation architecture. Given that Finnish and Estonian belong to the same language family and are similar, we use as training data the combination of the datasets of both language pairs to paliate the data scarceness of each individual pair. We also report the translation quality of systems trained on individual language pair data to serve as baseline and comparison reference. |
| Tasks | Machine Translation |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6406/ |
| https://www.aclweb.org/anthology/W18-6406 | |
| PWC | https://paperswithcode.com/paper/the-talp-upc-machine-translation-systems-for |
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Interpretable Emoji Prediction via Label-Wise Attention LSTMs
| Title | Interpretable Emoji Prediction via Label-Wise Attention LSTMs |
| Authors | Francesco Barbieri, Luis Espinosa-Anke, Jose Camacho-Collados, Steven Schockaert, Horacio Saggion |
| Abstract | Human language has evolved towards newer forms of communication such as social media, where emojis (i.e., ideograms bearing a visual meaning) play a key role. While there is an increasing body of work aimed at the computational modeling of emoji semantics, there is currently little understanding about what makes a computational model represent or predict a given emoji in a certain way. In this paper we propose a label-wise attention mechanism with which we attempt to better understand the nuances underlying emoji prediction. In addition to advantages in terms of interpretability, we show that our proposed architecture improves over standard baselines in emoji prediction, and does particularly well when predicting infrequent emojis. |
| Tasks | Emotion Recognition, Information Retrieval, Language Modelling, Machine Translation, Sentiment Analysis |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1508/ |
| https://www.aclweb.org/anthology/D18-1508 | |
| PWC | https://paperswithcode.com/paper/interpretable-emoji-prediction-via-label-wise |
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Overview of NLPTEA-2018 Share Task Chinese Grammatical Error Diagnosis
| Title | Overview of NLPTEA-2018 Share Task Chinese Grammatical Error Diagnosis |
| Authors | Gaoqi Rao, Qi Gong, Baolin Zhang, Endong Xun |
| Abstract | This paper presents the NLPTEA 2018 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 20 teams registered for this shared task, 13 teams developed the system and submitted a total of 32 runs. Progress in system performances was obviously, reaching F1 of 36.12{%} in position level and 25.27{%} in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers. |
| Tasks | Grammatical Error Correction |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3706/ |
| https://www.aclweb.org/anthology/W18-3706 | |
| PWC | https://paperswithcode.com/paper/overview-of-nlptea-2018-share-task-chinese |
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The AFRL WMT18 Systems: Ensembling, Continuation and Combination
| Title | The AFRL WMT18 Systems: Ensembling, Continuation and Combination |
| Authors | Jeremy Gwinnup, Tim Anderson, Grant Erdmann, Katherine Young |
| Abstract | This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT18 evaluation campaign. This year, we examined the developments and additions to popular neural machine translation toolkits and measure improvements in performance on the Russian{–}English language pair. |
| Tasks | Machine Translation |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6411/ |
| https://www.aclweb.org/anthology/W18-6411 | |
| PWC | https://paperswithcode.com/paper/the-afrl-wmt18-systems-ensembling |
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#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment
| Title | #MeToo Alexa: How Conversational Systems Respond to Sexual Harassment |
| Authors | Amanda Cercas Curry, Verena Rieser |
| Abstract | |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/papers/W18-0802/w18-0802 |
| https://www.aclweb.org/anthology/W18-0802v2 | |
| PWC | https://paperswithcode.com/paper/metoo-alexa-how-conversational-systems |
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A hybrid pipeline of rules and machine learning to filter web-crawled parallel corpora
| Title | A hybrid pipeline of rules and machine learning to filter web-crawled parallel corpora |
| Authors | Eduard Barbu, Verginica Barbu Mititelu |
| Abstract | A hybrid pipeline comprising rules and machine learning is used to filter a noisy web English-German parallel corpus for the Parallel Corpus Filtering task. The core of the pipeline is a module based on the logistic regression algorithm that returns the probability that a translation unit is accepted. The training set for the logistic regression is created by automatic annotation. The quality of the automatic annotation is estimated by manually labeling the training set. |
| Tasks | Machine Translation, Word Alignment |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6474/ |
| https://www.aclweb.org/anthology/W18-6474 | |
| PWC | https://paperswithcode.com/paper/a-hybrid-pipeline-of-rules-and-machine |
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Neural Clustering By Predicting And Copying Noise
| Title | Neural Clustering By Predicting And Copying Noise |
| Authors | Sam Coope, Andrej Zukov-Gregoric, Yoram Bachrach |
| Abstract | We propose a neural clustering model that jointly learns both latent features and how they cluster. Unlike similar methods our model does not require a predefined number of clusters. Using a supervised approach, we agglomerate latent features towards randomly sampled targets within the same space whilst progressively removing the targets until we are left with only targets which represent cluster centroids. To show the behavior of our model across different modalities we apply our model on both text and image data and very competitive results on MNIST. Finally, we also provide results against baseline models for fashion-MNIST, the 20 newsgroups dataset, and a Twitter dataset we ourselves create. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=BJvVbCJCb |
| https://openreview.net/pdf?id=BJvVbCJCb | |
| PWC | https://paperswithcode.com/paper/neural-clustering-by-predicting-and-copying |
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