Paper Group NANR 190
Tree-Stack LSTM in Transition Based Dependency Parsing. Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation. A Dataset of Flash and Ambient Illumination Pairs from the Crowd. DeepEMGNet: An Application for Efficient Discrimination of ALS and Normal EMG Signals. Stochastic Composite Mirror Descent: Optimal Bounds with …
Tree-Stack LSTM in Transition Based Dependency Parsing
| Title | Tree-Stack LSTM in Transition Based Dependency Parsing |
| Authors | {"O}mer K{\i}rnap, Erenay Dayan{\i}k, Deniz Yuret |
| Abstract | We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack{'}s σ-LSTM, buffer{'}s β-LSTM, actions{'} LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the {``}KParse{''} team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages. | |
| Tasks | Dependency Parsing, Lemmatization, Morphological Analysis, Morphological Tagging, Transition-Based Dependency Parsing, Word Embeddings |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-2012/ |
| https://www.aclweb.org/anthology/K18-2012 | |
| PWC | https://paperswithcode.com/paper/tree-stack-lstm-in-transition-based |
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Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation
| Title | Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation |
| Authors | Zhiqiang Xu |
| Abstract | Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations. In this work, we adopt an inexact Riemannian gradient descent perspective to investigate this technique on the effect of the step-size scheme. The shift-and-inverted power method is included as a special case with adaptive step-sizes. Particularly, two other step-size settings, i.e., constant step-sizes and Barzilai-Borwein (BB) step-sizes, are examined theoretically and/or empirically. We present a novel convergence analysis for the constant step-size setting that achieves a rate at $\tilde{O}(\sqrt{\frac{\lambda_{1}}{\lambda_{1}-\lambda_{p+1}}})$, where $\lambda_{i}$ represents the $i$-th largest eigenvalue of the given real symmetric matrix and $p$ is the multiplicity of $\lambda_{1}$. Our experimental studies show that the proposed algorithm can be significantly faster than the shift-and-inverted power method in practice. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7547-gradient-descent-meets-shift-and-invert-preconditioning-for-eigenvector-computation |
| http://papers.nips.cc/paper/7547-gradient-descent-meets-shift-and-invert-preconditioning-for-eigenvector-computation.pdf | |
| PWC | https://paperswithcode.com/paper/gradient-descent-meets-shift-and-invert |
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A Dataset of Flash and Ambient Illumination Pairs from the Crowd
| Title | A Dataset of Flash and Ambient Illumination Pairs from the Crowd |
| Authors | Yagiz Aksoy, Changil Kim, Petr Kellnhofer, Sylvain Paris, Mohamed Elgharib, Marc Pollefeys, Wojciech Matusik |
| Abstract | Illumination is a critical element of photography and is essential for many computer vision tasks. Flash light is unique in the sense that it is a widely available tool for easily manipulating the scene illumination. We present a dataset of thousands of ambient and flash illumination pairs to enable studying flash photography and other applications that can benefit from having separate illuminations. Different than the typical use of crowdsourcing in generating computer vision datasets, we make use of the crowd to directly take the photographs that make up our dataset. As a result, our dataset covers a wide variety of scenes captured by many casual photographers. We detail the advantages and challenges of our approach to crowdsourcing as well as the computational effort to generate completely separate flash illuminations from the ambient light in an uncontrolled setup. We present a brief examination of illumination decomposition, a challenging and underconstrained problem in flash photography, to demonstrate the use of our dataset in a data-driven approach. |
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| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Yagiz_Aksoy_A_Dataset_of_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Yagiz_Aksoy_A_Dataset_of_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/a-dataset-of-flash-and-ambient-illumination |
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DeepEMGNet: An Application for Efficient Discrimination of ALS and Normal EMG Signals
| Title | DeepEMGNet: An Application for Efficient Discrimination of ALS and Normal EMG Signals |
| Authors | Abdulkadir Sengur, Mehmet Gedikpinar, Yaman Akbulut, Erkan Deniz, Varun Bajaj, Yanhui Guo |
| Abstract | This paper proposes a deep learning application for efficient classification of amyotrophic lateral sclerosis (ALS) and normal Electromyogram (EMG) signals. EMG signals are helpful in analyzing of the neuromuscular diseases like ALS. ALS is a well-known brain disease, which progressively degenerates the motor neurons. Most of the previous works about EMG signal classification covers a dozen of basic signal processing methodologies such as statistical signal processing, wavelet analysis, and empirical mode decomposition (EMD). In this work, a different application is implemented which is based on time-frequency (TF) representation of EMG signals and convolutional neural networks (CNN). Short Time Fourier Transform (STFT) is considered for TF representation. Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.69% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method. |
| Tasks | ALS Detection, Electromyography (EMG) |
| Published | 2018-01-01 |
| URL | https://doi.org/10.1007/978-3-319-65960-2_77 |
| https://www.researchgate.net/publication/319189757_DeepEMGNet_An_Application_for_Efficient_Discrimination_of_ALS_and_Normal_EMG_Signals | |
| PWC | https://paperswithcode.com/paper/deepemgnet-an-application-for-efficient |
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Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities
| Title | Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities |
| Authors | Yunwen Lei, Ke Tang |
| Abstract | We study stochastic composite mirror descent, a class of scalable algorithms able to exploit the geometry and composite structure of a problem. We consider both convex and strongly convex objectives with non-smooth loss functions, for each of which we establish high-probability convergence rates optimal up to a logarithmic factor. We apply the derived computational error bounds to study the generalization performance of multi-pass stochastic gradient descent (SGD) in a non-parametric setting. Our high-probability generalization bounds enjoy a logarithmical dependency on the number of passes provided that the step size sequence is square-summable, which improves the existing bounds in expectation with a polynomial dependency and therefore gives a strong justification on the ability of multi-pass SGD to overcome overfitting. Our analysis removes boundedness assumptions on subgradients often imposed in the literature. Numerical results are reported to support our theoretical findings. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7425-stochastic-composite-mirror-descent-optimal-bounds-with-high-probabilities |
| http://papers.nips.cc/paper/7425-stochastic-composite-mirror-descent-optimal-bounds-with-high-probabilities.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-composite-mirror-descent-optimal |
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Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition
| Title | Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition |
| Authors | Yu Hong, Yang Xu, Huibin Ruan, Bowei Zou, Jianmin Yao, Guodong Zhou |
| Abstract | Event relation recognition is a challenging language processing task. It is required to determine the relation class of a pair of query events, such as causality, under the condition that there isn{'}t any reliable clue for use. We follow the traditional statistical approach in this paper, speculating the relation class of the target events based on the relation-class distributions on the similar events. There is minimal supervision used during the speculation process. In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events. We test our method on the ACE-R2 corpus and compared our model with the fully-supervised neural network models. Experimental results show that we achieve a comparable performance to CNN while slightly better than LSTM. |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1015/ |
| https://www.aclweb.org/anthology/C18-1015 | |
| PWC | https://paperswithcode.com/paper/incorporating-image-matching-into-knowledge |
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Information Nutrition Labels: A Plugin for Online News Evaluation
| Title | Information Nutrition Labels: A Plugin for Online News Evaluation |
| Authors | Vincentius Kevin, Birte H{"o}gden, Claudia Schwenger, Ali {\c{S}}ahan, Neelu Madan, Piush Aggarwal, Anusha Bangaru, Farid Muradov, Ahmet Aker |
| Abstract | In this paper we present a browser plugin \textit{NewsScan} that assists online news readers in evaluating the quality of online content they read by providing \textit{information nutrition labels} for online news articles. In analogy to groceries, where nutrition labels help consumers make choices that they consider best for themselves, information nutrition labels tag online news articles with data that help readers judge the articles they engage with. This paper discusses the choice of the labels, their implementation and visualization. |
| Tasks | Decision Making |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5505/ |
| https://www.aclweb.org/anthology/W18-5505 | |
| PWC | https://paperswithcode.com/paper/information-nutrition-labels-a-plugin-for |
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Character Level Convolutional Neural Network for German Dialect Identification
| Title | Character Level Convolutional Neural Network for German Dialect Identification |
| Authors | Mohamed Ali |
| Abstract | This submission is a description paper for our system in GDI shared task |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-3919/ |
| https://www.aclweb.org/anthology/W18-3919 | |
| PWC | https://paperswithcode.com/paper/character-level-convolutional-neural-network-1 |
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Scalable Wide and Deep Learning for Computer Assisted Coding
| Title | Scalable Wide and Deep Learning for Computer Assisted Coding |
| Authors | Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joel Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, Yi Zhang |
| Abstract | In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. As a first step towards reimbursement healthcare institutions need to associate ICD-10 billing codes to these documents. This is done by trained clinical coders who may use a computer assisted solution for shortlisting of codes. In this work, we present our work to build a machine learning based scalable system for predicting ICD-10 codes from electronic medical records. We address data imbalance issues by implementing two system architectures using convolutional neural networks and logistic regression models. We illustrate the pros and cons of those system designs and show that the best performance can be achieved by leveraging the advantages of both using a system combination approach. |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-3001/ |
| https://www.aclweb.org/anthology/N18-3001 | |
| PWC | https://paperswithcode.com/paper/scalable-wide-and-deep-learning-for-computer |
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Bit-Regularized Optimization of Neural Nets
| Title | Bit-Regularized Optimization of Neural Nets |
| Authors | Mohamed Amer, Aswin Raghavan, Graham W. Taylor, Sek Chai |
| Abstract | We present a novel regularization strategy for training neural networks which we call ``BitNet’'. The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over a real valued range. Our key idea is to control the expressive power of the network by dynamically quantizing the range and set of values that the parameters can take. We formulate this idea using a novel end-to-end approach that regularizes a typical classification loss function. Our regularizer is inspired by the Minimum Description Length (MDL) principle. For each layer of the network, our approach optimizes a translation and scaling factor along with integer-valued parameters. We empirically compare BitNet to an equivalent unregularized model on the MNIST and CIFAR-10 datasets. We show that BitNet converges faster to a superior quality solution. Additionally, the resulting model is significantly smaller in size due to the use of integer instead of floating-point parameters. | |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=HJg1NTGZRZ |
| https://openreview.net/pdf?id=HJg1NTGZRZ | |
| PWC | https://paperswithcode.com/paper/bit-regularized-optimization-of-neural-nets |
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CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets
| Title | CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets |
| Authors | Naveen J R, Barathi Ganesh H. B., An Kumar M, , Soman K P |
| Abstract | This paper discusses on task 1, {``}Affect in Tweets{''} sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based on the provided tweet data and also measure the intensity of these emotions for subsequent subtasks. Our approach in these task was to come up with a model on count based representation and use machine learning techniques for regression and classification related tasks. In this work, we use a simple bag of words technique for supervised text classification model as to compare, that even with some advance distributed representation models we can still achieve significant accuracy. Further, fine tuning on various parameters for the bag of word, representation model we acquired better scores over various other baseline models (Vinayan et al.) participated in the sharedtask. | |
| Tasks | Sentiment Analysis, Text Classification |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1049/ |
| https://www.aclweb.org/anthology/S18-1049 | |
| PWC | https://paperswithcode.com/paper/cennlp-at-semeval-2018-task-1-constrained |
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Listwise temporal ordering of events in clinical notes
| Title | Listwise temporal ordering of events in clinical notes |
| Authors | Serena Jeblee, Graeme Hirst |
| Abstract | We present metrics for listwise temporal ordering of events in clinical notes, as well as a baseline listwise temporal ranking model that generates a timeline of events that can be used in downstream medical natural language processing tasks. |
| Tasks | Information Retrieval, Relation Extraction |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-5620/ |
| https://www.aclweb.org/anthology/W18-5620 | |
| PWC | https://paperswithcode.com/paper/listwise-temporal-ordering-of-events-in |
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EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption
| Title | EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption |
| Authors | Liyuan Zhou, Qiongkai Xu, Hanna Suominen, Tom Gedeon |
| Abstract | This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media {—} more precisely, Twitter {—} with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users{'} tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances. |
| Tasks | Sentiment Analysis, Text Classification, Transfer Learning, Twitter Sentiment Analysis |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1071/ |
| https://www.aclweb.org/anthology/S18-1071 | |
| PWC | https://paperswithcode.com/paper/epution-at-semeval-2018-task-2-emoji |
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A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task
| Title | A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task |
| Authors | Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, Zhenglu Yang |
| Abstract | Enabling a mechanism to understand a temporal story and predict its ending is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). In this paper, we develop a multi-attention-based neural network (MANN) with well-designed optimizations, like Highway Network, and concatenated features with embedding representations into the hierarchical neural network model. Considering the particulars of the specific task, we thoughtfully extend MANN with external knowledge resources, exceeding state-of-the-art results obviously. Furthermore, we develop a thorough understanding of our model through a careful hand analysis on a subset of the stories. We identify what traits of MANN contribute to its outperformance and how external knowledge is obtained in such an ending prediction task. |
| Tasks | Common Sense Reasoning, Feature Engineering, Text Generation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1149/ |
| https://www.aclweb.org/anthology/C18-1149 | |
| PWC | https://paperswithcode.com/paper/a-multi-attention-based-neural-network-with |
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Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
| Title | Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation |
| Authors | Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan |
| Abstract | In this paper, we study the problems of principle Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficient algorithm for these problems. We prove the global convergence of our algorithm, borrowing ideas from the theory of fast-mixing Markov chains and two-Time-Scale Stochastic Approximation, showing that it achieves the optimal rate of convergence. In the process, we develop tools for understanding stochastic processes with Markovian noise which might be of independent interest. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7933-gen-oja-simple-efficient-algorithm-for-streaming-generalized-eigenvector-computation |
| http://papers.nips.cc/paper/7933-gen-oja-simple-efficient-algorithm-for-streaming-generalized-eigenvector-computation.pdf | |
| PWC | https://paperswithcode.com/paper/gen-oja-simple-efficient-algorithm-for |
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