Paper Group NANR 131
Distributed Multi-Player Bandits - a Game of Thrones Approach. Finite-state morphological analysis for Gagauz. Building Parallel Monolingual Gan Chinese Dialects Corpus. Deep Learning for Musculoskeletal Force Prediction. Document-Level Information as Side Constraints for Improved Neural Patent Translation. Large-scale spectral clustering using dif …
Distributed Multi-Player Bandits - a Game of Thrones Approach
| Title | Distributed Multi-Player Bandits - a Game of Thrones Approach |
| Authors | Ilai Bistritz, Amir Leshem |
| Abstract | We consider a multi-armed bandit game where N players compete for K arms for T turns. Each player has different expected rewards for the arms, and the instantaneous rewards are independent and identically distributed. Performance is measured using the expected sum of regrets, compared to the optimal assignment of arms to players. We assume that each player only knows her actions and the reward she received each turn. Players cannot observe the actions of other players, and no communication between players is possible. We present a distributed algorithm and prove that it achieves an expected sum of regrets of near-O\left(\log^{2}T\right). This is the first algorithm to achieve a poly-logarithmic regret in this fully distributed scenario. All other works have assumed that either all players have the same vector of expected rewards or that communication between players is possible. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7952-distributed-multi-player-bandits-a-game-of-thrones-approach |
| http://papers.nips.cc/paper/7952-distributed-multi-player-bandits-a-game-of-thrones-approach.pdf | |
| PWC | https://paperswithcode.com/paper/distributed-multi-player-bandits-a-game-of |
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Finite-state morphological analysis for Gagauz
| Title | Finite-state morphological analysis for Gagauz |
| Authors | Francis Tyers, Sevilay Bayatli, G{"u}ll{"u} Karanfil, Memduh G{"o}k{\i}rmak, Francis M. Tyers |
| Abstract | |
| Tasks | Morphological Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1411/ |
| https://www.aclweb.org/anthology/L18-1411 | |
| PWC | https://paperswithcode.com/paper/finite-state-morphological-analysis-for |
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Building Parallel Monolingual Gan Chinese Dialects Corpus
| Title | Building Parallel Monolingual Gan Chinese Dialects Corpus |
| Authors | Fan Xu, Mingwen Wang, Maoxi Li |
| Abstract | |
| Tasks | Language Identification |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1036/ |
| https://www.aclweb.org/anthology/L18-1036 | |
| PWC | https://paperswithcode.com/paper/building-parallel-monolingual-gan-chinese |
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Deep Learning for Musculoskeletal Force Prediction
| Title | Deep Learning for Musculoskeletal Force Prediction |
| Authors | Lance Rane, Ziyun Ding, Alison H. McGregor, Anthony M. J. Bull |
| Abstract | Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in the use of supervised learning to build approximate models for computationally demanding processes, with benefits in speed and flexibility. Here, we use a deep neural network to learn the mapping from movement space to muscle space. Trained on a set of kinematic, kinetic and electromyographic measurements from 156 subjects during gait, the network’s predictions of internal force magnitudes show good concordance with those derived by musculoskeletal modelling. In a separate set of experiments, training on data from the most widely known benchmarks of modelling performance, the international Grand Challenge competitions, generates predictions that better those of the winning submissions in four of the six competitions. Computational speedup facilitates incorporation into a lab-based system permitting real-time estimation of forces, and interrogation of the trained neural networks provides novel insights into population-level relationships between kinematic and kinetic factors. |
| Tasks | Electromyography (EMG), EMG Signal Prediction, Medial knee JRF Prediction, Muscle Force Prediction |
| Published | 2018-12-13 |
| URL | https://doi.org/10.1007/s10439-018-02190-0 |
| https://link.springer.com/content/pdf/10.1007%2Fs10439-018-02190-0.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-for-musculoskeletal-force |
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Document-Level Information as Side Constraints for Improved Neural Patent Translation
| Title | Document-Level Information as Side Constraints for Improved Neural Patent Translation |
| Authors | Laura Jehl, Stefan Riezler |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-03-01 |
| URL | https://www.aclweb.org/anthology/W18-1802/ |
| https://www.aclweb.org/anthology/W18-1802 | |
| PWC | https://paperswithcode.com/paper/document-level-information-as-side |
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Large-scale spectral clustering using diffusion coordinates on landmark-based bipartite graphs
| Title | Large-scale spectral clustering using diffusion coordinates on landmark-based bipartite graphs |
| Authors | Khiem Pham, Guangliang Chen |
| Abstract | Spectral clustering has received a lot of attention due to its ability to separate nonconvex, non-intersecting manifolds, but its high computational complexity has significantly limited its applicability. Motivated by the document-term co-clustering framework by Dhillon (2001), we propose a landmark-based scalable spectral clustering approach in which we first use the selected landmark set and the given data to form a bipartite graph and then run a diffusion process on it to obtain a family of diffusion coordinates for clustering. We show that our proposed algorithm can be implemented based on very efficient operations on the affinity matrix between the given data and selected landmarks, thus capable of handling large data. Finally, we demonstrate the excellent performance of our method by comparing with the state-of-the-art scalable algorithms on several benchmark data sets. |
| Tasks | Semantic Segmentation |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-1705/ |
| https://www.aclweb.org/anthology/W18-1705 | |
| PWC | https://paperswithcode.com/paper/large-scale-spectral-clustering-using |
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ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets
| Title | ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets |
| Authors | Jos{'e}-{'A}ngel Gonz{'a}lez, Llu{'\i}s-F. Hurtado, Ferran Pla |
| Abstract | This paper describes the participation of ELiRF-UPV team at tasks 1 and 3 of Semeval-2018. We present a deep learning based system that assembles Convolutional Neural Networks and Long Short-Term Memory neural networks. This system has been used with slight modifications for the two tasks addressed both for English and Spanish. Finally, the results obtained in the competition are reported and discussed. |
| Tasks | Emotion Classification, Sentiment Analysis |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1092/ |
| https://www.aclweb.org/anthology/S18-1092 | |
| PWC | https://paperswithcode.com/paper/elirf-upv-at-semeval-2018-tasks-1-and-3 |
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What Action Causes This? Towards Naive Physical Action-Effect Prediction
| Title | What Action Causes This? Towards Naive Physical Action-Effect Prediction |
| Authors | Qiaozi Gao, Shaohua Yang, Joyce Chai, V, Lucy erwende |
| Abstract | Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples. |
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| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1086/ |
| https://www.aclweb.org/anthology/P18-1086 | |
| PWC | https://paperswithcode.com/paper/what-action-causes-this-towards-naive |
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Improving Topic Quality by Promoting Named Entities in Topic Modeling
| Title | Improving Topic Quality by Promoting Named Entities in Topic Modeling |
| Authors | Katsiaryna Krasnashchok, Salim Jouili |
| Abstract | News related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper we use named entities as domain-specific terms for news-centric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity. |
| Tasks | Named Entity Recognition |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-2040/ |
| https://www.aclweb.org/anthology/P18-2040 | |
| PWC | https://paperswithcode.com/paper/improving-topic-quality-by-promoting-named |
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Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task
| Title | Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task |
| Authors | Ramesh Manuvinakurike, Trung Bui, Walter Chang, Kallirroi Georgila |
| Abstract | We present {``}conversational image editing{''}, a novel real-world application domain combining dialogue, visual information, and the use of computer vision. We discuss the importance of dialogue incrementality in this task, and build various models for incremental intent identification based on deep learning and traditional classification algorithms. We show how our model based on convolutional neural networks outperforms models based on random forests, long short term memory networks, and conditional random fields. By training embeddings based on image-related dialogue corpora, we outperform pre-trained out-of-the-box embeddings, for intention identification tasks. Our experiments also provide evidence that incremental intent processing may be more efficient for the user and could save time in accomplishing tasks. | |
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| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-5033/ |
| https://www.aclweb.org/anthology/W18-5033 | |
| PWC | https://paperswithcode.com/paper/conversational-image-editing-incremental |
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Coreference and Focus in Reading Times
| Title | Coreference and Focus in Reading Times |
| Authors | Evan Jaffe, Cory Shain, William Schuler |
| Abstract | |
| Tasks | Coreference Resolution |
| Published | 2018-01-01 |
| URL | https://www.aclweb.org/anthology/W18-0101/ |
| https://www.aclweb.org/anthology/W18-0101 | |
| PWC | https://paperswithcode.com/paper/coreference-and-focus-in-reading-times |
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Deep Continuous Fusion for Multi-Sensor 3D Object Detection
| Title | Deep Continuous Fusion for Multi-Sensor 3D Object Detection |
| Authors | Ming Liang, Bin Yang, Shenlong Wang, Raquel Urtasun |
| Abstract | In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art. |
| Tasks | 3D Object Detection, Object Detection |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deep-continuous-fusion-for-multi-sensor-3d |
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Affordances in Grounded Language Learning
| Title | Affordances in Grounded Language Learning |
| Authors | Stephen McGregor, KyungTae Lim |
| Abstract | We present a novel methodology involving mappings between different modes of semantic representation. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner{'}s direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning. |
| Tasks | Question Answering |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2806/ |
| https://www.aclweb.org/anthology/W18-2806 | |
| PWC | https://paperswithcode.com/paper/affordances-in-grounded-language-learning |
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Landmark based localization in urban environment
| Title | Landmark based localization in urban environment |
| Authors | Xiaozhi Qu, Bahman Soheilian, Nicolas Paparoditis |
| Abstract | A landmark based localization with uncertainty analysis based on cameras and geo-referenced landmarks is presented in this paper. The system is developed to adapt different camera configurations for six degree-of-freedom pose estimation. Local bundle adjustment is applied for optimization and the geo- referenced landmarks are integrated to reduce the drift. In particular, the uncertainty analysis is taken into account. On the one hand, we estimate the uncertainties of poses to predict the precision of localiza- tion. On the other hand, uncertainty propagation is considered for matching, tracking and landmark reg- istering. The proposed method is evaluated on both KITTI benchmark and the data acquired by a mobile mapping system. In our experiments, decimeter level accuracy can be reached. |
| Tasks | Pose Estimation |
| Published | 2018-01-01 |
| URL | https://www.sciencedirect.com/science/article/abs/pii/S0924271617302228 |
| https://pan.baidu.com/s/1xx4mfpMkZVyj9bRtmiTLRg | |
| PWC | https://paperswithcode.com/paper/landmark-based-localization-in-urban |
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Errator: a Tool to Help Detect Annotation Errors in the Universal Dependencies Project
| Title | Errator: a Tool to Help Detect Annotation Errors in the Universal Dependencies Project |
| Authors | Guillaume Wisniewski |
| Abstract | |
| Tasks | Cross-Lingual Transfer, Machine Translation, Tokenization |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1711/ |
| https://www.aclweb.org/anthology/L18-1711 | |
| PWC | https://paperswithcode.com/paper/errator-a-tool-to-help-detect-annotation |
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