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493 results on '"Garcia-Duran A"'

1. Efficient Entity Candidate Generation for Low-Resource Languages

Author

García-Durán, Alberto, Arora, Akhil, and West, Robert

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Candidate generation is a crucial module in entity linking. It also plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. Nevertheless, it has often been overlooked in the monolingual English entity linking literature, as naive approaches obtain very good performance. Unfortunately, the existing approaches for English cannot be successfully transferred to poorly resourced languages. This paper constitutes an in-depth analysis of the candidate generation problem in the context of cross-lingual entity linking with a focus on low-resource languages. Among other contributions, we point out limitations in the evaluation conducted in previous works. We introduce a characterization of queries into types based on their difficulty, which improves the interpretability of the performance of different methods. We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches. A thorough empirical analysis on 9 real-world datasets under 2 evaluation settings shows that our simple solution outperforms the state-of-the-art approach in terms of both quality and efficiency for almost all datasets and query types., Comment: LREC 2022

Published
2022

2. ‘It’s a delicate topic’: Stigma, capabilities and young people’s mental health in post-conflict Colombia

Author

Sara Donetto, Shari Ortiz Baddan Sochandamandou, Maria Camila Garcia Duran, Philipp Hessel, Annie Zimmerman, Ricardo Araya Baltra, and Fabio Idrobo

Subjects
Mental health, stigma, capabilities approach, co-design, Colombia, Public aspects of medicine, RA1-1270
Abstract

ABSTRACTYoung people in Colombia present high rates of mental health problems, to which the country’s history of armed internal conflict contributes in complex ways. Mental health services in Colombia are fragmented, inadequate, and difficult to access for many. Young people’s help-seeking is often hindered by mental health stigma and/or poor experiences with services. This paper presents a thematic analysis of qualitative data from a mixed-methods study aimed at developing and testing a mental health intervention for Colombian youths. We draw upon theoretical lenses from scholarly work on stigma and Sen’s ‘capabilities approach’ to inform our analysis of interviews and group discussions with staff and young people involved in the state-funded human capital building programme ‘Jovenes en Acción’ (JeA). By illustrating how study participants talked about stigma, vulnerability, mental health services organisation, and the challenges of discussing mental health topics in a learning environment, we illuminate aspects of mental health support and anti-stigma interventions that might need enhancing. In particular, we suggest that more emphasis on ‘community competencies’ as complementary to and interrelated with individual competencies would strengthen young people’s individual and collective resources for mental wellbeing while being in line with the sociocritical principles of existing human capital-enhancing programmes.

Published
2024
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4. Wikipedia Reader Navigation: When Synthetic Data Is Enough

Author

Arora, Akhil, Gerlach, Martin, Piccardi, Tiziano, García-Durán, Alberto, and West, Robert

Subjects
Computer Science - Computers and Society, Computer Science - Digital Libraries, Computer Science - Social and Information Networks
Abstract

Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy., Comment: WSDM 2022, 11 pages, 16 figures

Published
2022
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5. Commerce and Security Meet in the European Union’s Trade Defence Instruments

Author

Patricia Garcia-Duran, L. Johan Eliasson, and Oriol Costa

Subjects
economic security strategy, european union, liberal international order, security, strategic autonomy, trade instruments, trade policy, Political science (General), JA1-92
Abstract

Mercantilist policies, protectionism, Chinese and US violations of the spirit—if not always the rules—of the World Trade Organization, along with supply chain vulnerabilities, trade wars, and illegal state subsidies have all contributed to a rise in the weaponisation of commerce (using trade in response to, or to achieve, political decisions or acts) across the globe. The weaponisation and geo-politicisation of trade pose a challenge for the EU, which is poorly suited for a game of power politics. Its common commercial policy developed separately from the intergovernmental foreign and security policy. The level of exclusive EU competence differs across the two policy domains, as do decision-making processes. Drawing on work addressing ideational and instrumental levels of policy, we discuss how the EU is assessing the international environment through the ideational framework of strategic autonomy, and how this has shaped the construction of new trade defence instruments intended to protect against economic and technology-related security risks. Focusing specifically on trade defence instruments addressing security concerns, which are justified in the 2023 European Economic Security Strategy (especially in the pillar focusing on protecting against economic security risks), we show that the distinction between commercial policy and traditional security concerns is eroding.

Published
2023
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7. Low-Rank Subspaces for Unsupervised Entity Linking

Author

Arora, Akhil, García-Durán, Alberto, and West, Robert

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availability of entity names and a referent knowledge base. Eigenthemes exploits the fact that the entities that are truly mentioned in a document (the "gold entities") tend to form a semantically dense subset of the set of all candidate entities in the document. Geometrically speaking, when representing entities as vectors via some given embedding, the gold entities tend to lie in a low-rank subspace of the full embedding space. Eigenthemes identifies this subspace using the singular value decomposition and scores candidate entities according to their proximity to the subspace. On the empirical front, we introduce multiple strong baselines that compare favorably to (and sometimes even outperform) the existing state of the art. Extensive experiments on benchmark datasets from a variety of real-world domains showcase the effectiveness of our approach., Comment: EMNLP 2021, 18 pages, 22 figures

Published
2021
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8. Recursive input and state estimation: A general framework for learning from time series with missing data

Author

García-Durán, Alberto and West, Robert

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state to account for the missing observations, and then treat the transformed signal in a standard manner. In this paper, we introduce a single unifying framework, Recursive Input and State Estimation (RISE), for this general approach and reformulate existing models as specific instances of this framework. We then explore additional novel variations within the RISE framework to improve the performance of any instance. We exploit representation learning techniques to learn latent representations of the signals used by RISE instances. We discuss and develop various encoding techniques to learn latent signal representations. We benchmark instances of the framework with various encoding functions on three data imputation datasets, observing that RISE instances always benefit from encoders that learn representations for numerical values from the digits into which they can be decomposed., Comment: Published at ICASSP 2021

Published
2021

9. Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation

Author

Bayram, Eda, Garcia-Duran, Alberto, and West, Robert

Subjects
Computer Science - Machine Learning
Abstract

The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, denoted as MrAP, imputes the values of missing attributes by propagating information across the multi-relational structure of a knowledge graph. It employs regression functions for predicting one node attribute from another depending on the relationship between the nodes and the type of the attributes. The propagation mechanism operates iteratively in a message passing scheme that collects the predictions at every iteration and updates the value of the node attributes. Experiments over two benchmark datasets show the effectiveness of our approach., Comment: 7 pages

Published
2020

10. Clinical Outcomes in Patients With Bicuspid Aortic Valves and Ascending Aorta ≥50 mm Under Surveillance

Author

Ye, Zi, Lane, Colleen E., Beachey, Joel D., Medina-Inojosa, Jose, Galian-Gay, Laura, Dentamaro, Ilaria, Rodriguez-Palomares, Jose, Calvo-Iglesias, Francisco, Paz, Rafael Cobas, Alegret, Josep M., Sanchez, Violeta, Moral, Sergio, Bellino, Michele, Citro, Rodolfo, Enriquez-Sarano, Maurice, Bagnati, Rodrigo P., Garcia Duran, Ana B., Evangelista, Arturo, and Michelena, Hector I.

Published
2023
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12. MMKG: Multi-Modal Knowledge Graphs

Author

Liu, Ye, Li, Hui, Garcia-Duran, Alberto, Niepert, Mathias, Onoro-Rubio, Daniel, and Rosenblum, David S.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs.We validate the utility ofMMKG in the sameAs link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types., Comment: ESWC 2019

Published
2019

13. Learning Representations of Missing Data for Predicting Patient Outcomes

Author

Malone, Brandon, Garcia-Duran, Alberto, and Niepert, Mathias

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, such as multivariate time series, free text, and categorical demographic information; important relationships among patients can be difficult to detect; and many others. In this work, we propose a novel approach to address these first three challenges using a representation learning scheme based on message passing. We show that our proposed approach is competitive with or outperforms the state of the art for predicting in-hospital mortality (binary classification), the length of hospital visits (regression) and the discharge destination (multiclass classification).

Published
2018

14. Thermoluminescent characteristics of seven varieties of quartz

Author

Márquez-Mata, Claudia Angélica, Vega-Carrillo, Hector Rene, Mata-Chávez, Ma Jesus, Garcia-Reyna, Mayra Guadalupe, Vazquez-Bañuelos, Joel, Campillo-Rivera, Guillermo Eduardo, García-Duran, Ángel, Torres-Cortes, Carina Oliva, Rosales-Candelas, Ileana, and Soto-Bernal, Juan José

Published
2023
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17. Knowledge Graph Completion to Predict Polypharmacy Side Effects

Author

Malone, Brandon, García-Durán, Alberto, and Niepert, Mathias

Subjects
Computer Science - Databases, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation., Comment: 13th International Conference on Data Integration in the Life Sciences (DILS2018)

Published
2018

18. Learning Sequence Encoders for Temporal Knowledge Graph Completion

Author

García-Durán, Alberto, Dumančić, Sebastijan, and Niepert, Mathias

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license 1., Comment: EMNLP'18

Published
2018

19. A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning

Author

Dumancic, Sebastijan, Garcia-Duran, Alberto, and Niepert, Mathias

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks such as knowledge base completion. These approaches abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare representation learning and relational learning on various relational classification and clustering tasks and analyse the complexity of the rules used implicitly by these approaches. Preliminary results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs., Comment: corrected version: incorrect evaluation fixed; IJCAI 2019

Published
2018

20. Towards a Spectrum of Graph Convolutional Networks

Author

Niepert, Mathias and Garcia-Duran, Alberto

Subjects
Computer Science - Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

We present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies. Based on an analysis of GCNs with the help of the corresponding computation graphs, we propose a generalization of existing GCNs where the aggregation operations are (a) determined by structural properties of the local neighborhood graphs and (b) not restricted to weighted averages. We show that the proposed approach is strictly more expressive while requiring only a modest increase in the number of parameters and computations. We also show that the proposed generalization is identical to standard convolutional layers when applied to regular grid graphs.

Published
2018

21. TransRev: Modeling Reviews as Translations from Users to Items

Author

Garcia-Duran, Alberto, Gonzalez, Roberto, Onoro-Rubio, Daniel, Niepert, Mathias, and Li, Hui

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract

The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TransRev learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed items. This allows TransRev to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TransRev outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.

Published
2018

23. Advances in immunotherapy for cervical cancer

Author

Juan Francisco Grau-Bejar, Carmen Garcia-Duran, David Garcia-Illescas, Oriol Mirallas, and Ana Oaknin

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Cervical cancer still represents a major public health problem, being the fourth most common cancer in incidence and mortality in women worldwide. These figures are unacceptable since cervical cancer, an human papillomavirus-related malignancy, is a largely preventable disease by means of well-established screening and vaccination programs. Patients with recurrent, persistent, or metastatic disease unsuitable for curative therapeutic approaches represent a dismal prognosis population. Until recently, these patients were only candidates for cisplatin-based chemotherapy plus bevacizumab. However, the introduction of immune checkpoint inhibitors has revolutionized the treatment landscape of this disease achieving historical overall survival improvements in both the post-platinum and frontline settings. Interestingly, the clinical development of immunotherapy in cervical cancer is currently advancing to earlier stages of the disease, as the locally advanced setting, whose standard of care has not changed in the last decades with still modest outcomes. As more innovative immunotherapy approaches are in clinical early development in advanced cervical cancer, promising efficacy data are emerging that may shape the future of this disease. This review summarizes the main treatment advances carried out in the field of immunotherapy throughout the past years.

Published
2023
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25. Learning Graph Representations with Embedding Propagation

Author

Garcia-Duran, Alberto and Niepert, Mathias

Subjects
Computer Science - Learning
Abstract

We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data. EP learns vector representations of graphs by passing two types of messages between neighboring nodes. Forward messages consist of label representations such as representations of words and other attributes associated with the nodes. Backward messages consist of gradients that result from aggregating the label representations and applying a reconstruction loss. Node representations are finally computed from the representation of their labels. With significantly fewer parameters and hyperparameters an instance of EP is competitive with and often outperforms state of the art unsupervised and semi-supervised learning methods on a range of benchmark data sets.

Published
2017

26. KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

Author

Garcia-Duran, Alberto and Niepert, Mathias

Subjects
Computer Science - Artificial Intelligence
Abstract

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN., Comment: UAI 2018

Published
2017

27. Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs

Author

Oñoro-Rubio, Daniel, Niepert, Mathias, García-Durán, Alberto, González, Roberto, and López-Sastre, Roberto J.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images. We explore novel machine learning approaches for answering visual-relational queries in web-extracted knowledge graphs. To this end, we have created ImageGraph, a KG with 1,330 relation types, 14,870 entities, and 829,931 images crawled from the web. With visual-relational KGs such as ImageGraph one can introduce novel probabilistic query types in which images are treated as first-class citizens. Both the prediction of relations between unseen images as well as multi-relational image retrieval can be expressed with specific families of visual-relational queries. We introduce novel combinations of convolutional networks and knowledge graph embedding methods to answer such queries. We also explore a zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. The resulting multi-relational grounding of unseen entity images into a knowledge graph serves as a semantic entity representation. We conduct experiments to demonstrate that the proposed methods can answer these visual-relational queries efficiently and accurately.

Published
2017

28. Net2Vec: Deep Learning for the Network

Author

Gonzalez, Roberto, Manco, Filipe, Garcia-Duran, Alberto, Mendes, Jose, Huici, Felipe, Niccolini, Saverio, and Niepert, Mathias

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Learning
Abstract

We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network. Net2Vec is able to capture data from the network at more than 60Gbps, transform it into meaningful tuples and apply predictions over the tuples in real time. This platform can be used for different purposes ranging from traffic classification to network performance analysis. Finally, we showcase the use of Net2Vec by implementing and testing a solution able to profile network users at line rate using traces coming from a real network. We show that the use of deep learning for this case outperforms the baseline method both in terms of accuracy and performance.

Published
2017

29. Tuning the spin-crossover properties of [Fe2] metal-organic cages.

Author

Navarro, Laia, Garcia-Duran, Arnau, and Cirera, Jordi

Subjects
*MOLECULAR orbitals, *TRANSITION temperature, *LIGANDS (Chemistry), *ELECTRONIC structure, *TEMPERATURE sensors
Abstract

A computational study on the interplay between ligand functionalization and guest effects on the transition temperature (T1/2) in the [Fe2(L1R)3]@X (L1 = 1,3-bis-(3-(pyridin-2-yl)-1H-pyrazol-5-yl)benzene, X = H-, F-, Cl-, Br-, I- and [BF4]-, R = H, F, or CH3) family of metal-organic cages (MOCs) is presented. Our results indicate that ligand functionalization with electron-donating or electron-withdrawing groups can significantly impact the T1/2 as expected, while the guest effect in lowering the T1/2 has a linear correlation with the increasing guest size. More importantly, small guests can move away from the center of the cavity, thus enhancing the two-step characteristic of the transition. All the data can be understood by analyzing the underlying electronic structure of the studied systems in terms of the relevant d-based molecular orbitals. These results can help in the rational design of new MOCs that can operate as sensors at specific temperatures, thus accelerating the discovery of new SCO devices with tailored properties. [ABSTRACT FROM AUTHOR]

Published
2024
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30. A pre-specified analysis of the Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease (DAPA-CKD) randomized controlled trial on the incidence of abrupt declines in kidney function

Author

Heerspink, Hiddo J.L., Wheeler, David C., Chertow, Glenn, Correa-Rotter, Ricardo, Greene, Tom, Hou, Fan Fan, McMurray, John, Rossing, Peter, Toto, Robert, Stefansson, Bergur, Langkilde, Anna Maria, Maffei, L.E., Raffaele, P., Solis, S.E., Arias, C.A., Aizenberg, D., Luquez, C., Zaidman, C., Cluigt, N., Mayer, M., Alvarisqueta, A., Wassermann, A., Maldonado, R., Bittar, J., Maurich, M., Gaite, L.E., Garcia, N., Sivak, L., Ramallo, P.O., Santos, J.C., Garcia Duran, R., Oddino, J.A., Maranon, A., Maia, L.N., Avila, D.D., Barros, E.J.G., Vidotti, M.H., Panarotto, D., Noronha, I.D.L., Turatti, L.A.A., Deboni, L., Canziani, M.E., Riella, M.C., Bacci, M.R., Paschoalin, R.P., Franco, R.J., Goldani, J.C., St-Amour, E., Steele, A.W., Goldenberg, R., Pandeya, S., Bajaj, H., Cherney, D., Kaiser, S.M., Conway, J.R., Chow, S.S., Bailey, G., Lafrance, J., Winterstein, J., Cournoyer, S., Gaudet, D., Madore, F., Houlden, R.L., Dowell, A., Langlois, M., Muirhead, N., Khandwala, H., Levin, A., Hou, F., Xue, Y., Zuo, L., Hao, C., Ni, Z., Xing, C., Chen, N., Dong, Y., Zhou, R., Xiao, X., Zou, Y., Wang, C., Liu, B., Chen, Q., Lin, M., Luo, Q., Zhang, D., Wang, J., Chen, M., Wang, X., Zhong, A., Dong, J., Zhu, C., Yan, T., Luo, P., Ren, Y., Pai, P., Li, D., Zhang, R., Zhang, J., Xu, M., Zhuang, Y., Kong, Y., Yao, X., Peng, X., Persson, F.I., Hansen, T.K., Borg, R., Pedersen Bjergaard, U., Hansen, D., Hornum, M., Haller, H., Klausmann, G., Tschope, D., Kruger, T., Gross, P., Hugo, C., Obermuller, N., Rose, L., Mertens, P., Zeller-Stefan, H., Fritsche, A., Renders, L., Muller, J., Budde, K., Schroppel, B., Wittmann, I., Voros, P., Dudas, M., Tabak, G.A., Kirschner, R., Letoha, A., Balku, I., Hermanyi, Z., Zakar, G., Mezei, I., Nagy, G.G., Lippai, J., Nemeth, A., Khullar, D., Gowdaiah, P.K., Fernando Mervin, E., Rao, V.A., Dewan, D., Goplani, K., Maddi, V.S.K., Vyawahare, M.S., Pulichikkat, R.K., Pandey, R., Sonkar, S.K., Gupta, V.K., Agarwal, S., Asirvatham, A.J., Ignatius, A., Chaubey, S., Melemadathil, S., Alva, H., Kadam, Y., Shimizu, H., Sueyoshi, A., Takeoka, H., Abe, Y., Imai, T., Onishi, Y., Fujita, Y., Tokita, Y., Oura, M., Makita, Y., Idogaki, A., Koyama, R., Kikuchi, H., Kashihara, N., Hayashi, T., Ando, Y., Tanaka, T., Shimizu, M., Hidaka, S., Gohda, T., Tamura, K., Abe, M., Kamijo, Y., Imasawa, T., Takahashi, Y., Nakayama, M., Tomita, M., Hirano, F., Fukushima, Y., Kiyosue, A., Kurioka, S., Imai, E., Kitagawa, K., Waki, M., Wada, J., Uehara, K., Iwatani, H., Ota, K., Shibazaki, S., Katayama, K., Narita, I., Iinuma, M., Matsueda, S., Sasaki, S., Yokochi, A., Tsukamoto, T., Yoshimura, T., Kang, S., Lee, S., Lim, C.S., Chin, H., Joo, K.W., Han, S.Y., Chang, T.I., Park, S., Park, H., Park, C.W., Han, B.G., Cha, D.R., Yoon, S.A., Kim, W., Kim, S.W., Ryu, D., Correa Rotter, R., Irizar Santana, S.S., Hernandez Llamas, G., Valdez Ortiz, R., Secchi Nicolas, N.C., Gonzalez Galvez, G., Lazcano Soto, J.R., Bochicchio Riccardelli, T., Bayram Llamas, E.A., Ramos Ibarra, D.R., Melo, M.G.S., Gonzalez Gonzalez, J.G., Sanchez Mijangos, J.H., Madero Robalo, M., Garcia Castillo, A., Manrique, H.A., Farfan, J.C., Vargas, R., Valdivia, A., Dextre, A., Escudero, E., Calderon Ticona, J.R., Gonzales, L., Villena, J., Leon, L., Molina, G., Saavedra, A., Garrido, E., Arbanil, H., Vargas Marquez, S., Rodriguez, J., Isidto, R., Villaflor, A.J., Gumba, M.A., Tirador, L., Comia, R.S., Sy, R.A., Guanzon, M.L.V.V., Aquitania, G., De Asis, N.C., Silva, A.A., Romero, C.M., Lim, M.E., Danguilan, R.A., Nowicki, M., Rudzki, H., Landa, K., Kucharczyk-Bauman, I., Gogola-Migdal, B., Golski, M., Olech-Cudzik, A., Stompor, T., Szczepanik, T., Miklaszewicz, B., Sciborski, R., Kuzniewski, M., Ciechanowski, K., Wronska, D., Klatko, W., Mazur, S., Popenda, G., Myslicki, M., Bolieva, L.Z., Berns, S., Galyavich, A., Abissova, T., Karpova, I., Platonov, D., Koziolova, N., Kvitkova, L., Nilk, R., Medina, T., Rebrov, A., Rossovskaya, M., Sinitsina, I., Vishneva, E., Zagidullin, N., Novikova, T., Krasnopeeva, N., Magnitskaya, O., Antropenko, N., Batiushin, M., Escudero Quesada, V., Barrios Barrea, C., Espinel Garauz, E., Cruzado Garrit, J.M., Morales Portillo, C., Gorriz Teruel, J.L., Cigarran Guldris, S., Praga Terente, M., Robles Perez-Monteoliva, N.R., Tinahones Madueno, F.J., Soto Gonzalez, A., Diaz Rodriguez, C., Furuland, H., Saeed, A., Dreja, K., Spaak, J., Bruchfeld, A., Kolesnyk, M., Levchenko, O., Pyvovarova, N., Stus, V., Doretskyy, V., Korobova, N., Horoshko, O., Katerenchuk, I., Mostovoy, Y.M., Orynchak, M., Legun, O., Dudar, I., Bilchenko, O., Andreychyn, S., Levchenko, A., Zub, L., Tereshchenko, N., Topchii, I., Ostapenko, T., Bezuglova, S., Kopytsya, M., Turenko, O., Mark, P., Barratt, J., Bhandari, S., Fraser, D., Kalra, P., Kon, S.P., Mccafferty, K., Mikhail, A., Alvarado, O.P., Anderson, R., Andrawis, N.S., Arif, A., Benjamin, S.A., Bueso, G., Busch, R.S., Carr, K.W., Crawford, P., Daboul, N., De La Calle, G.M., Delgado, B., Earl, J., El-Shahawy, M.A., Graf, R.J., Greenwood, G., Guevara, A., Wendland, E.M., Mayfield, R.K., Montero, M., Morin, D.J., Narayan, P., Numrungroad, V., Reddy, A.C., Reddy, R., Samson, M.B., Trejo, R., Butcher, M.B., Wise, J.K., Zemel, L.R., Raikhel, M., Weinstein, D., Hernandez, P., Wynne, A., Khan, B.V., Sterba, G.A., Jamal, A., Ross, D., Rovner, S.F., Tan, A., Ovalle, F., Patel, R.J., Talano, J., Patel, D.R., Burgner, A., Aslam, N., Elliott, M., Goral, S., Jovanovich, A., Manley, J.A., Umanath, K., Waguespack, D., Weiner, D., Yu, M., Schneider, L., Jalal, D., Le, T., Nguyen, N., Nguyen, H., Nguyen, D., Nguyen, V., Do, T., Chu, P., Ta, D., Tran, N., Pham, B., Pfeffer, Marc A., Pocock, Stuart, Swedberg, Karl, Rouleau, Jean L., Chaturvedi, Nishi, Ivanovich, Peter, Levey, Andrew S., Christ-Schmidt, Heidi, Held, Claes, Christersson, Christina, Mann, Johannes, Varenhorst, Christoph, Cherney, David, Postmus, Douwe, Stefánsson, Bergur V., Chertow, Glenn M., Dwyer, Jamie P., Kosiborod, Mikhail, McMurray, John J.V., Sjöström, C. David, and Toto, Robert D.

Published
2022
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36. Theory and Method

Author

Eliasson, Leif Johan, Huet, Patricia Garcia-Duran, Eliasson, Leif Johan, and Huet, Patricia Garcia-Duran

Published
2019
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38. Supplementary Table S3 from Phase I Trial of First-line Bintrafusp Alfa in Patients with Locally Advanced or Persistent/Recurrent/Metastatic Cervical Cancer

Author

Oaknin, Ana, primary, Ghamande, Sharad A., primary, Kasamatsu, Yuka, primary, Gil-Martin, Marta, primary, Grau-Bejar, Juan Francisco, primary, Garcia-Duran, Carmen, primary, Sato, Masashi, primary, Siddiqui, Abdul, primary, Chaudhary, Surendra Pal, primary, Vugmeyster, Yulia, primary, and Hasegawa, Kosei, primary

Published
2024
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39. Supplementary Figure S3 from Phase I Trial of First-line Bintrafusp Alfa in Patients with Locally Advanced or Persistent/Recurrent/Metastatic Cervical Cancer

Author

Oaknin, Ana, primary, Ghamande, Sharad A., primary, Kasamatsu, Yuka, primary, Gil-Martin, Marta, primary, Grau-Bejar, Juan Francisco, primary, Garcia-Duran, Carmen, primary, Sato, Masashi, primary, Siddiqui, Abdul, primary, Chaudhary, Surendra Pal, primary, Vugmeyster, Yulia, primary, and Hasegawa, Kosei, primary

Published
2024
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40. Data from Phase I Trial of First-line Bintrafusp Alfa in Patients with Locally Advanced or Persistent/Recurrent/Metastatic Cervical Cancer

Author

Oaknin, Ana, primary, Ghamande, Sharad A., primary, Kasamatsu, Yuka, primary, Gil-Martin, Marta, primary, Grau-Bejar, Juan Francisco, primary, Garcia-Duran, Carmen, primary, Sato, Masashi, primary, Siddiqui, Abdul, primary, Chaudhary, Surendra Pal, primary, Vugmeyster, Yulia, primary, and Hasegawa, Kosei, primary

Published
2024
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41. Supplementary Methods S1 from Phase I Trial of First-line Bintrafusp Alfa in Patients with Locally Advanced or Persistent/Recurrent/Metastatic Cervical Cancer

Author

Oaknin, Ana, primary, Ghamande, Sharad A., primary, Kasamatsu, Yuka, primary, Gil-Martin, Marta, primary, Grau-Bejar, Juan Francisco, primary, Garcia-Duran, Carmen, primary, Sato, Masashi, primary, Siddiqui, Abdul, primary, Chaudhary, Surendra Pal, primary, Vugmeyster, Yulia, primary, and Hasegawa, Kosei, primary

Published
2024
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43. TransRev: Modeling Reviews as Translations from Users to Items

Author

García-Durán, Alberto, González, Roberto, Oñoro-Rubio, Daniel, Niepert, Mathias, Li, Hui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jose, Joemon M., editor, Yilmaz, Emine, editor, Magalhães, João, editor, Castells, Pablo, editor, Ferro, Nicola, editor, Silva, Mário J., editor, and Martins, Flávio, editor

Published
2020
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44. Lipoprotein(a) and Benefit of PCSK9 Inhibition in Patients With Nominally Controlled LDL Cholesterol

Author

Schwartz, Gregory G., Gabriel Steg, P.h., Bhatt, Deepak L., Bittner, Vera A., Diaz, Rafael, Goodman, Shaun G., Harrington, Robert A., Jukema, J. Wouter, Szarek, Michael, White, Harvey D., Zeiher, Andreas M., Tricoci, Pierluigi, Roe, Matthew T., Mahaffey, Kenneth W., Edelberg, Jay M., Hanotin, Corinne, Lecorps, Guillaume, Moryusef, Angèle, Pordy, Robert, Sasiela, William J., Tamby, Jean-François, Aylward, Philip E., Drexel, Heinz, Sinnaeve, Peter, Dilic, Mirza, Lopes, Renato D., Gotcheva, Nina N., Prieto, Juan-Carlos, Yong, Huo, López-Jaramillo, Patricio, Pećin, Ivan, Reiner, Zeljko, Ostadal, Petr, Poulsen, Steen Hvitfeldt, Viigimaa, Margus, Nieminen, Markku S., Danchin, Nicolas, Chumburidze, Vakhtang, Marx, Nikolaus, Liberopoulos, Evangelos, Montenegro Valdovinos, Pablo Carlos, Tse, Hung-Fat, Kiss, Robert Gabor, Xavier, Denis, Zahger, Doron, Valgimigli, Marco, Kimura, Takeshi, Kim, Hyo Soo, Kim, Sang-Hyun, Erglis, Andrejs, Laucevicius, Aleksandras, Kedev, Sasko, Yusoff, Khalid, Ramos López, Gabriel Arturo, Alings, Marco, Halvorsen, Sigrun, Correa Flores, Roger M., Sy, Rody G., Budaj, Andrzej, Morais, Joao, Dorobantu, Maria, Karpov, Yuri, Ristic, Arsen D., Chua, Terrance, Murin, Jan, Fras, Zlatko, Dalby, Anthony J., Tuñón, José, Asita de Silva, H., Hagström, Emil, Landmesser, Ulf, Müller, Christian, Chiang, Chern-En, Sritara, Piyamitr, Guneri, Sema, Parkhomenko, Alexander, Ray, Kausik K., Moriarty, Patrick M., Vogel, Robert, Chaitman, Bernard, Kelsey, Sheryl F., Olsson, Anders G., Rouleau, Jean-Lucien, Simoons, Maarten L., Alexander, Karen, Meloni, Chiara, Rosenson, Robert, Sijbrands, Eric J.G., Alexander, John H., Armaganijan, Luciana, Bagai, Akshay, Bahit, Maria Cecilia, Brennan, J. Matthew, Clifton, Shaun, DeVore, Adam D., Deloatch, Shalonda, Dickey, Sheila, Dombrowski, Keith, Ducrocq, Grégory, Eapen, Zubin, Endsley, Patricia, Eppinger, Arleen, Harrison, Robert W., Hess, Connie Ng, Hlatky, Mark A., Jordan, Joseph Dedrick, Knowles, Joshua W., Kolls, Bradley J., Kong, David F., Leonardi, Sergio, Lillis, Linda, Maron, David J., Marcus, Jill, Mathews, Robin, Mehta, Rajendra H., Mentz, Robert J., Moreira, Humberto Graner, Patel, Chetan B., Pereira, Sabrina Bernardez, Perkins, Lynn, Povsic, Thomas J., Puymirat, Etienne, Jones, William Schuyler, Shah, Bimal R., Sherwood, Matthew W., Stringfellow, Kenya, Sujjavanich, Darin, Toma, Mustafa, Trotter, Charlene, van Diepen, Sean F.P., Wilson, Matthew D., Tze-Kay Yan, Andrew, Schiavi, Lilia B., Garrido, Marcelo, Alvarisqueta, Andrés F., Sassone, Sonia A., Bordonava, Anselmo P., Alves De Lima, Alberto E., Schmidberg, Jorge M., Duronto, Ernesto A., Caruso, Orlando C., Novaretto, Leonardo P., Hominal, Miguel Angel, Montaña, Oscar R., Caccavo, Alberto, Gomez Vilamajo, Oscar A., Lorenzatti, Alberto J., Cartasegna, Luis R., Paterlini, Gustavo A., Mackinnon, Ignacio J., Caime, Guillermo D., Amuchastegui, Marcos, Salomone, Oscar, Codutti, Oscar R., Jure, Horacio O., Bono, Julio O.E., Hrabar, Adrian D., Vallejos, Julio A., Ahuad Guerrero, Rodolfo A., Novoa, Federico, Patocchi, Cristian A., Zaidman, Cesar J., Giuliano, Maria E., Dran, Ricardo D., Vico, Marisa L., Carnero, Gabriela S., Guzman, Pablo N., Medrano Allende, Juan C., Garcia Brasca, Daniela F., Bustamante Labarta, Miguel H., Nani, Sebastian, Blumberg, Eduardo D.S., Colombo, Hugo R., Liberman, Alberto, Fuentealba, Victorino, Luciardi, Hector L., Waisman, Gabriel D., Berli, Mario A., Garcia Duran, Ruben O., Cestari, Horacio G., Luquez, Hugo A., Giordano, Jorge A., Saavedra, Silvia S., Zapata, Gerardo, Costamagna, Osvaldo, Llois, Susana, Waites, Jonathon H., Collins, Nicholas, Soward, Allan, Hii, Chris L.S., Shaw, James, Arstall, Margaret A., Horowitz, John, Ninio, Daniel, Rogers, James F., Colquhoun, David, Oqueli Flores, Romulo E., Roberts-Thomson, Philip, Raffel, Owen, Lehman, Sam J., Aroney, Constantine, Coverdale, Steven G.M., Garrahy, Paul J., Starmer, Gregory, Sader, Mark, Carroll, Patrick A., Dick, Ronald, Zweiker, Robert, Hoppe, Uta, Huber, Kurt, Berger, Rudolf, Delle-Karth, Georg, Frey, Bernhard, Faes, Dirk, Hermans, Kurt, Pirenne, Bruno, Leone, Attilio, Hoffer, Etienne, Vrolix, Mathias C.M., De Wolf, Luc, Wollaert, Bart, Castadot, Marc, Dujardin, Karl, Beauloye, Christophe, Vervoort, Geert, Striekwold, Harry, Convens, Carl, Roosen, John, Barbato, Emanuele, Claeys, Marc, Cools, Frank, Terzic, Ibrahim, Barakovic, Fahir, Midzic, Zlatko, Pojskic, Belma, Fazlibegovic, Emir, Durak-Nalbantic, Azra, Kulić, Mehmed, Vulic, Dusko, Muslibegovic, Adis, Goronja, Boris, Reis, Gilmar, Sousa, Luciano, Nicolau, Jose C., Giorgeto, Flavio E., Silva, Ricardo P., Maia, Lilia Nigro, Rech, Rafael, Rossi, Paulo R.F., Cerqueira, Maria José A.G., Duda, 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Senaratne, Manohara, Stone, James, Kornder, Jan, Pearce, Stephen, Dion, Danielle, Savard, Daniel, Pesant, Yves, Pandey, Amritanshu, Robinson, Simon, Gosselin, Gilbert, Vizel, Saul, Hoag, Gordon, Bourgeois, Ronald, Morisset, Anne, Sabbah, Eric, Sussex, Bruce, Kouz, Simon, MacDonald, Paul, Diaz, Ariel, Michaud, Nicolas, Fell, David, Leung, Raymond, Vuurmans, Tycho, Lai, Christopher, Nigro, Frank, Davies, Richard, Nogareda, Gustavo, Vijayaraghavan, Ram, Ducas, John, Lepage, Serge, Mehta, Shamir, Cha, James, Dupuis, Robert, Fong, Peter, Lutchmedial, Sohrab, Rodes-Cabau, Josep, Fadlallah, Hussein, Cleveland, David, Huynh, Thao, Bata, Iqbal, Hameed, Adnan, Pincetti, Cristian, Potthoff, Sergio, Prieto, Juan C., Acevedo, Monica, Aguirre, Arnoldo, Vejar, Margarita, Yañez, Mario, Araneda, Guillermo, Fernandez, Mauricio, Perez, Luis, Varleta, Paola, Florenzano, Fernando, Huidobro, Laura, Raffo, Carlos A., Olivares, Claudia, Nahuelpan, Leonardo, Montecinos, Humberto, Chen, Jiyan, Dong, Yugang, Huang, Weijian, Wang, Jianzhong, Huang, Shi'An, Yao, Zhuhua, Li, Xiang, Cui, Lan, Lin, Wenhua, Sun, Yuemin, Wang, Jingfeng, Li, Jianping, Zhang, Xuelian, Zhu, Hong, Chen, Dandan, Huang, Lan, Dong, Shaohong, Su, Guohai, Xu, Biao, Su, Xi, Cheng, Xiaoshu, Lin, Jinxiu, Zong, Wenxia, Li, Huanming, Feng, Yi, Xu, Dingli, Yang, Xinchun, Ke, Yuannan, Lin, Xuefeng, Zhang, Zheng, Zheng, Zeqi, Luo, Zhurong, Chen, Yundai, Ding, Chunhua, Zhong, Yi, Zheng, Yang, Li, Xiaodong, Peng, Daoquan, Zhao, Shuiping, Li, Ying, Liu, Xuebo, Wei, Meng, Liu, Shaowen, Yu, Yihua, Qu, Baiming, Jiang, Weihong, Zhou, Yujie, Zhao, Xingsheng, Yuan, Zuyi, Guo, Ying, Xu, Xiping, Shi, Xubo, Ge, Junbo, Fu, Guosheng, Bai, Feng, Fang, Weiyi, Shou, Xiling, Yang, Xiangjun, Wang, Jian'An, Xiang, Meixiang, Sun, Yingxian, Lu, Qinghua, Zhang, Ruiyan, Zhu, Jianhua, Xu, Yizhou, Fan, Zhongcai, Li, Tianchang, Wu, Chun, Jaramillo, Nicolas, Vallejo, Gregorio Sanchez, Luna Botia, Diana C., Lopez, Rodrigo Botero, Molina De Salazar, Dora I., Cadena Bonfanti, Alberto J., Aroca, Carlos Cotes, Higuera, Juan Diego, Blanquicett, Marco, Barrera Silva, Sandra I., Garcia Lozada, Henry J., Coronel Arroyo, Julian A., Accini Mendoza, Jose L., Fernandez Ruiz, Ricardo L., Quintero Ossa, Alvaro M., Manzur Jatin, Fernando G., Herazo, Aristides Sotomayor, Parada, Jeffrey Castellanos, Arambula, Rafael Suarez, Urina Triana, Miguel A., Fernandez Trujillo, Angela M., Strozzi, Maja, Car, Siniša, Jerić, Melita, Miličić, Davor, Benčić, Martina Lovrić, Pintarić, Hrvoje, Prvulović, Đeiti, Šikić, Jozica, Peršić, Viktor, Mileta, Dean, Štambuk, Kresimir, Babić, Zdravko, Tomulic, Vjekoslav, Lukenda, Josip, Mejic-Krstulovic, Stanka, Starcevic, Boris, Spinar, Jindrich, Horak, David, Velicka, Zdenek, Stasek, Josef, Alan, David, Machova, Vilma, Linhart, Ales, Novotny, Vojtech, Kaucak, Vladimir, Rokyta, Richard, Naplava, Robert, Coufal, Zdenek, Adamkova, Vera, Podpera, Ivo, Zizka, Jiri, Motovska, Zuzana, Marusincova, Ivana, Svab, Premysl, Heinc, Petr, 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Geargios, Skoumas, Ioannis, Athyros, Vasilios, Vardas, Panagiotis, Parthenakis, Frangkiskos, Lekakis, John, Hatzitolios, Apostolos, Fausto Ovando, Sergio R., Arango Benecke, Juan L., Rodriguez De Leon, Edgar R., Yan, Bryan P.Y., Siu, David C.W., Turi, Tibor, Merkely, Bela, Ungi, Imre, Lupkovics, Geza, Nagy, Lajos, Katona, András, Édes, István, Müller, Gábor, Horvath, Iván, Kapin, Tibor, Szigeti, Zsolt, Faluközy, József, Kumbla, Mukund, Sandhu, Manjinder, Annam, Sharath, Proddutur, Naveen Reddy, Regella, Reddy, Premchand, Rajendra K., Mahajan, Ajaykumar, Pawar, Sudhir, Abhyanakar, Atul D., Kerkar, Prafulla, Govinda, Ravishankar A., Oomman, Abraham, Sinha, Dhurjati, Patil, Sachin N., Kahali, Dhiman, Sawhney, Jitendra, Joshi, Abhijeet B., Chaudhary, Sanjeev, Harkut, Pankaj, Guha, Santanu, Porwal, Sanjay, Jujjuru, Srimannarayana, Pothineni, Ramesh B., Monteiro, Minguel R., Khan, Aziz, Iyengar, Shamanna S., Grewal, Jasprakash Singh, Chopda, Manoj, Fulwani, Mahesh C., Patange, Aparna, 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Rolando J., Hernandez Zuniga, Jose D., Camacho Cosavalente, Luis A., Bravo Mannucci, Jorge E., Landeo, Javier Heredia, Llerena Navarro, Nassip C., Roldan Concha, Yudy M., Rodriguez Chavez, Víctor E., Anchante Hernandez, Henry A., Zea Nunez, Carlos A., Ramos, Walter Mogrovejo, Ferrolino, Arthur, Sy, Rosa Allyn G., Tirador, Louie, Matiga, Generoso, Coching, Raul Martin, Bernan, Alisa, Rogelio, Gregorio, Morales, Dante D., Tan, Edgar, Sulit, Dennis Jose, Wlodarczak, Adrian, Jaworska, Krystyna, Skonieczny, Grzegorz, Pawlowicz, Lidia, Wojewoda, Pawel, Busz-Papiez, Benita, Bednarski, Janusz, Goch, Aleksander, Staneta, Pawel, Dulak, Elzbieta, Saminski, Krzysztof, Krasowski, Wlodzimierz, Sudnik, Wanda, Zurakowski, Aleksander, Skorski, Marcin, Lysek, Roman, Miklaszewicz, Beata, Kubica, Jacek, Lipko, Jan Andrzej, Kostarska-Srokosz, Edyta, Piepiorka, Marek, Drzewiecka, Anna, Sciborski, Ryszard, Stasiewski, Arkadiusz, Blicharski, Tomasz, Bystryk, Leszek, Szpajer, Michal, Korol, Marek, Czerski, 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Pericas, Laia Matas, Zamorano, Jose Luis, De Mora Martin, Manuel, Cortada, Jordi Bruguera, Alonso Martin, Joaquin J., Serrano Antolin, Jose Maria, De Berrazueta Fernández, José R., Vázquez de Prada, José Antonio, Díaz Fernández, Jose Francisco, García Lledó, José Alberto, Sales, Juan Cosín, Rodriguez, Javier Botas, Tragant, Gabriel Gusi, Benedicto, Amparo, Gonzalez-Juanatey, Carlos, Potau, Mercedes Camprubí, Perez, Ignacio Plaza, De La Tassa, César Morís, Loma-Osorio Rincon, Pablo, Recena, Javier Balaguer, Escudier, Juan M., Payeras, Antonio Coca, Orcajo, Norberto Alonso, Valdivielso, Pedro, Constantine, Godwin, Haniffa, Ruvaiz, Tissera, Nirmali, Amarasekera, Stanley, Ponnamperuma, Chandrike, Fernando, Nimali, Fernando, Kaputella, Jayawardena, Jayanthimala, Wijeyasingam, Santharaj, Ranasinghe, Gotabhaya, Ekanayaka, Ruvan, Mendis, Sepalika, Senaratne, Vajira, Mayurathan, Gnanamoorthy, Rajapaksha, Ajantha, Sirisena, Thilak, Herath, Jagath I., Amarasena, Naomali, Berglund, Stefan, 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2021
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45. Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Author

Serban, Iulian Vlad, García-Durán, Alberto, Gulcehre, Caglar, Ahn, Sungjin, Chandar, Sarath, Courville, Aaron, and Bengio, Yoshua

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing, H.3.4, I.5.1, I.2.6, I.2.7
Abstract

Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions., Comment: 13 pages, 1 figure, 7 tables

Published
2016

48. Apolipoprotein B, Residual Cardiovascular Risk After Acute Coronary Syndrome, and Effects of Alirocumab

Author

Hagström, Emil, Steg, P. Gabriel, Szarek, Michael, Bhatt, Deepak L., Bittner, Vera A., Danchin, Nicolas, Diaz, Rafael, Goodman, Shaun G., Harrington, Robert A., Jukema, J. Wouter, Liberopoulos, Evangelos, Marx, Nikolaus, McGinniss, Jennifer, Manvelian, Garen, Pordy, Robert, Scemama, Michel, White, Harvey D., Zeiher, Andreas M., Schwartz, Gregory G., Schwartz, Gregory G., Bhatt, Deepak L., Bittner, Vera A., Diaz, Rafael, Goodman, Shaun G., Harrington, Robert A., Jukema, J. Wouter, Szarek, Michael, White, Harvey D., Zeiher, Andreas M., Tricoci, Pierluigi, Roe, Matthew T., Mahaffey, Kenneth W., Edelberg, Jay M., Hanotin, Corinne, Lecorps, Guillaume, Moryusef, Angèle, Pordy, Robert, Sasiela, William J., Tamby, Jean-François, Diaz, Rafael, Aylward, Philip E, Drexel, Heinz, Sinnaeve, Peter, Dilic, Mirza, Lopes, Renato D., Gotcheva, Nina N, Goodman, Shaun G., Prieto, Juan-Carlos, Yong, Huo, López-Jaramillo, Patricio, Pećin, Ivan, Reiner, Zeljko, Ostadal, Petr, Poulsen, Steen Hvitfeldt, Viigimaa, Margus, Nieminen, Markku S, Danchin, Nicolas, Chumburidze, Vakhtang, Marx, Nikolaus, Liberopoulos, Evangelos, Carlos, Pablo, Valdovinos, Montenegro, Tse, Hung-Fat, Kiss, Robert Gabor, Xavier, Denis, Zahger, Doron, Valgimigli, Marco, Kimura, Takeshi, Kim, Hyo Soo, Kim, Sang-Hyun, Erglis, Andrejs, Laucevicius, Aleksandras, Kedev, Sasko, Yusoff, Khalid, Ramos López, Gabriel Arturo, Alings, Marco, White, Harvey D., Halvorsen, Sigrun, Correa Flores, Roger M, Sy, Rody G., Budaj, Andrzej, Morais, Joao, Dorobantu, Maria, Karpov, Yuri, Ristic, Arsen D., Chua, Terrance, Murin, Jan, Fras, Zlatko, Dalby, Anthony J, Tuñón, José, de Silva, H. Asita, Hagström, Emil, Landmesser, Ulf, Müller, Christian, Chiang, Chern-En, Sritara, Piyamitr, Guneri, Sema, Parkhomenko, Alexander, Ray, Kausik K., Moriarty, Patrick M., Roe, Matthew T., Vogel, Robert, Chaitman, Bernard, Kelsey, Sheryl F., Olsson, Anders G., Rouleau, Jean-Lucien, Simoons, Maarten L., Alexander, Karen, Meloni, Chiara, Rosenson, Robert, Sijbrands, Eric J.G., Tricoci, Pierluigi, Alexander, John H., Armaganijan, Luciana, Bagai, Akshay, Bahit, Maria Cecilia, Brennan, J. Matthew, Clifton, Shaun, DeVore, Adam D., Deloatch, Shalonda, Dickey, Sheila, Dombrowski, Keith, Ducrocq, Grégory, Eapen, Zubin, Endsley, Patricia, Eppinger, Arleen, Harrison, Robert W., Hess, Connie Ng, Hlatky, Mark A., Jordan, Joseph Dedrick, Knowles, Joshua W., Kolls, Bradley J., Kong, David F., Leonardi, Sergio, Lillis, Linda, Lopes, Renato D., Maron, David J., Mahaffey, Kenneth W., Marcus, Jill, Mathews, Robin, Mehta, Rajendra H., Mentz, Robert J., Moreira, Humberto Graner, Patel, Chetan B., Pereira, Sabrina Bernardez, Perkins, Lynn, Povsic, Thomas J., Puymirat, Etienne, Roe, Matthew T., Jones, William Schuyler, Shah, Bimal R., Sherwood, Matthew W., Stringfellow, Kenya, Sujjavanich, Darin, Toma, Mustafa, Trotter, Charlene, van Diepen, Sean F.P., Wilson, Matthew D., Tze-Kay Yan, Andrew, Schiavi, Lilia B, Garrido, Marcelo, Alvarisqueta, Andrés F, Sassone, Sonia A, Bordonava, Anselmo P, Alves De Lima, Alberto E, Schmidberg, Jorge M, Duronto, Ernesto A, Caruso, Orlando C, Novaretto, Leonardo P, Hominal, Miguel Angel, Montaña, Oscar R, Caccavo, Alberto, Gomez Vilamajo, Oscar A, Lorenzatti, Alberto J, Cartasegna, Luis R, Paterlini, Gustavo A, Mackinnon, Ignacio J, Caime, Guillermo D, Amuchastegui, Marcos, Salomone, Oscar, Codutti, Oscar R, Jure, Horacio O, Bono, Julio OE, Hrabar, Adrian D, Vallejos, Julio A, Ahuad Guerrero, Rodolfo A, Novoa, Federico, Patocchi, Cristian A, Zaidman, Cesar J, Giuliano, Maria E, Dran, Ricardo D, Vico, Marisa L, Carnero, Gabriela S, Guzman, Pablo N, Medrano Allende, Juan C, Garcia Brasca, Daniela F, Bustamante Labarta, Miguel H, Nani, Sebastian, Blumberg, Eduardo DS, Colombo, Hugo R, Liberman, Alberto, Fuentealba, Victorino, Luciardi, Hector L, Waisman, Gabriel D, Berli, Mario A, Garcia Duran, Ruben O, Cestari, Horacio G, Luquez, Hugo A, Giordano, Jorge A, Saavedra, Silvia S, Zapata, Gerardo, Costamagna, Osvaldo, Llois, Susana, Waites, Jonathon H, Collins, Nicholas, Soward, Allan, Aylward, Philip E, Hii, Chris LS, Aylward, Philip E, Shaw, James, Arstall, Margaret A, Horowitz, John, Ninio, Daniel, Rogers, James F, Colquhoun, David, Oqueli Flores, Romulo E, Roberts-Thomson, Philip, Raffel, Owen, Lehman, Sam J, Aroney, Constantine, Coverdale, Steven GM, Garrahy, Paul J, Starmer, Gregory, Sader, Mark, Carroll, Patrick A, Dick, Ronald, Zweiker, Robert, Hoppe, Uta, Drexel, Heinz, Huber, Kurt, Berger, Rudolf, Delle-Karth, Georg, Frey, Bernhard, Weidinger, Franz, Faes, Dirk, Hermans, Kurt, Pirenne, Bruno, Leone, Attilio, Hoffer, Etienne, Sinnaeve, Peter, Vrolix, Mathias CM, Wolf, Luc De, Wollaert, Bart, Castadot, Marc, Dujardin, Karl, Beauloye, Christophe, Vervoort, Geert, Striekwold, Harry, Convens, Carl, Roosen, John, Barbato, Emanuele, Claeys, Marc, Cools, Frank, Terzic, Ibrahim, Barakovic, Fahir, Midzic, Zlatko, Pojskic, Belma, Fazlibegovic, Emir, Dilic, Mirza, Durak-Nalbantic, Azra, Kulić, Mehmed, Vulic, Dusko, Muslibegovic, Adis, Goronja, Boris, Reis, Gilmar, Sousa, Luciano, Nicolau, Jose C, Giorgeto, Flavio E, Silva, Ricardo P, Maia, Lilia Nigro, Rech, Rafael, Rossi, Paulo RF, Cerqueira, Maria José AG, Duda, Norberto, Kalil, Renato, Kormann, Adrian, Abrantes, José Antonio M, Filho, Pedro Pimentel, Soggia, Ana Priscila, de Santos, Mayler ON, Neuenschwander, Fernando, Bodanese, Luiz C, Michalaros, Yorghos L, Eliaschewitz, Freddy G, Vidotti, Maria H, Leaes, Paulo E, Botelho, Roberto V, Kaiser, Sergio, Manenti, Euler Roberto F Fernandes, Precoma, Dalton B, Moura Jorge, Jose C, Silva, Pedro, Silveira, Jose A, Saporito, Wladmir, Neto, Jose A Marin, Feitosa, Gilson S, Ritt, Luiz Eduardo F, de Souza, Juliana A, Costa, Fernando, Souza, Weimar KSB, Reis, Helder JL, Lopes, Renato D., Machado, Leandro, Aidar Ayoub, José Carlos, Todorov, Georgi V, Nikolov, Fedya P, Velcheva, Elena S, Tzekova, Maria L, Benov, Haralambi O, Petranov, Stanislav L, Tumbev, Haralin S, Shehova-Yankova, Nina S, Markov, Dimitar T, Raev, Dimitar H, Mollov, Mihail N, Kichukov, Kostadin N, Ilieva- Pandeva, Katya A, Gotcheva, Nina N, Ivanova, Raya, Gospodinov, Maryana, Mincheva, Valentina M, Lazov, Petar V, Dimov, Bojidar I, Senaratne, Manohara, Stone, James, Kornder, Jan, Pearce, Stephen, Dion, Danielle, Savard, Daniel, Pesant, Yves, Pandey, Amritanshu, Robinson, Simon, Gosselin, Gilbert, Vizel, Saul, Hoag, Gordon, Bourgeois, Ronald, Morisset, Anne, Sabbah, Eric, Sussex, Bruce, Kouz, Simon, MacDonald, Paul, Diaz, Ariel, Michaud, Nicolas, Fell, David, Leung, Raymond, Vuurmans, Tycho, Lai, Christopher, Nigro, Frank, Davies, Richard, Nogareda, Gustavo, Vijayaraghavan, Ram, Ducas, John, Lepage, Serge, Mehta, Shamir, Cha, James, Dupuis, Robert, Fong, Peter, Lutchmedial, Sohrab, Rodes-Cabau, Josep, Fadlallah, Hussein, Cleveland, David, Huynh, Thao, Bata, Iqbal, Hameed, Adnan, Pincetti, Cristian, Potthoff, Sergio, Prieto, Juan C, Acevedo, Monica, Aguirre, Arnoldo, Vejar, Margarita, Yañez, Mario, Araneda, Guillermo, Fernandez, Mauricio, Perez, Luis, Varleta, Paola, Florenzano, Fernando, Huidobro, Laura, Raffo, Carlos A, Olivares, Claudia, Nahuelpan, Leonardo, Montecinos, Humberto, Chen, Jiyan, Dong, Yugang, Huang, Weijian, Wang, Jianzhong, Huang, Shi’An, Yao, Zhuhua, Li, Xiang, Cui, Lan, Lin, Wenhua, Sun, Yuemin, Wang, Jingfeng, Li, Jianping, Zhang, Xuelian, Zhu, Hong, Chen, Dandan, Huang, Lan, Dong, Shaohong, Su, Guohai, Xu, Biao, Su, Xi, Cheng, Xiaoshu, Lin, Jinxiu, Zong, Wenxia, Li, Huanming, Feng, Yi, Xu, Dingli, Yang, Xinchun, Ke, Yuannan, Lin, Xuefeng, Zhang, Zheng, Zheng, Zeqi, Luo, Zhurong, Chen, Yundai, Ding, Chunhua, Zhong, Yi, Zheng, Yang, Li, Xiaodong, Peng, Daoquan, Zhao, Shuiping, Li, Ying, Liu, Xuebo, Wei, Meng, Liu, Shaowen, Yu, Yihua, Qu, Baiming, Jiang, Weihong, Zhou, Yujie, Zhao, Xingsheng, Yuan, Zuyi, Guo, Ying, Xu, Xiping, Shi, Xubo, Ge, Junbo, Fu, Guosheng, Bai, Feng, Fang, Weiyi, Shou, Xiling, Yang, Xiangjun, Wang, Jian’An, Xiang, Meixiang, Sun, Yingxian, Lu, Qinghua, Zhang, Ruiyan, Zhu, Jianhua, Xu, Yizhou, Fan, Zhongcai, Li, Tianchang, Wu, Chun, Jaramillo, Nicolas, Vallejo, Gregorio Sanchez, Luna Botia, Diana C, Lopez, Rodrigo Botero, De Salazar, Dora I Molina, Bonfanti, Alberto J Cadena, Aroca, Carlos Cotes, Higuera, Juan Diego, Blanquicett, Marco, Barrera Silva, Sandra I, Garcia Lozada, Henry J, Coronel Arroyo, Julian A, Accini Mendoza, Jose L, Fernandez Ruiz, Ricardo L, Quintero Ossa, Alvaro M., Manzur Jatin, Fernando G, Sotomayor Herazo, Aristides, Parada, Jeffrey Castellanos, Arambula, Rafael Suarez, Urina Triana, Miguel A, Trujillo, Angela M Fernandez, Strozzi, Maja, Car, Siniša, Miličić, Davor, Lovrić Benčić, Martina, Pintarić, Hrvoje, Prvulović, Đeiti, Šikić, Jozica, Peršić, Viktor, Mileta, Dean, Štambuk, Kresimir, Babić, Zdravko, Tomulic, Vjekoslav, Lukenda, Josip, Mejic-Krstulovic, Stanka, Starcevic, Boris, Spinar, Jindrich, Horak, David, Velicka, Zdenek, Alan, David, Machova, Vilma, Linhart, Ales, Novotny, Vojtech, Kaucak, Vladimir, Rokyta, Richard, Naplava, Robert, Coufal, Zdenek, Adamkova, Vera, Podpera, Ivo, Zizka, Jiri, Motovska, Zuzana, Marusincova, Ivana, Ostadal, Petr, Heinc, Petr, Kuchar, Jiri, Povolny, Petr, Matuska, Jiri, Poulsen, Steen H, Raungaard, Bent, Clemmensen, Peter, Bang, Lia E, May, Ole, Bøttcher, Morten, Hove, Jens D, Frost, Lars, Gislason, Gunnar, Larsen, John, Johansen, Peter Betton, Hald, Flemming, Johansen, Peter, Jeppesen, Jørgen, Nielsen, Tonny, Kristensen, Kjeld S, Walichiewicz, Piotr Maria, Lomholdt, Jens D, Klausen, Ib C, Nielsen, Peter Kaiser, Davidsen, Flemming, Videbaek, Lars, Viigimaa, Margus, Soots, Mai, Vahula, Veiko, Hedman, Anu, Soopõld, Üllar, Märtsin, Kaja, Jurgenson, Tiina, Kristjan, Arved, Nieminen, Markku S, Vikman, Saila, Huikuri, Heikki, Airaksinen, Juhani, Coste, Pierre, Ferrari, Emile, Danchin, Nicolas, Morel, Olivier, Montalescot, Gilles, Machecourt, Jacques, Barone-Rochette, Gilles, Mansourati, Jacques, Cottin, Yves, Steg, Ph. Gabriel, Leclercq, Florence, Belhassane, Abdelkader, Delarche, Nicolas, Boccara, Franck, Paganelli, Franck, Clerc, Jérôme, Schiele, Francois, Aboyans, Victor, Probst, Vincent, Berland, Jacques, Lefèvre, Thierry, Chumburidze, Vakhtang, Khintibidze, Irakli, Shaburishvili, Tamaz, Pagava, Zurab, Ghlonti, Ramaz, Lominadze, Zaza, Khabeishvili, George, Hemetsberger, Rayyan, Edward, Kemala, Rauch-Kröhnert, Ursula, Stratmann, Matthias, Appel, Karl-Friedrich, Schmidt, Ekkehard, Omran, Heyder, Stellbrink, Christoph, Dorsel, Thomas, Lianopoulos, Emmanouil, Vöhringer, Hans Friedrich, Marx, Roger, Zirlik, Andreas, Schellenberg, Detlev, Heitzer, Thomas, Laufs, Ulrich, Werner, Christian, Marx, Nikolaus, Gielen, Stephan, Nuding, Sebastian, Winkelmann, Bernhard, Behrens, Steffen, Sydow, Karsten, Karakas, Mahir, Simonis, Gregor, Muenzel, Thomas, Werner, Nikos, Leggewie, Stefan, Böcker, Dirk, Braun- Dullaeus, Rüdiger, Toursarkissian, Nicole, Jeserich, Michael, Weißbrodt, Matthias, Schaeufele, Tim, Weil, Joachim, Völler, Heinz, Waltenberger, Johannes, Natour, Mohammed, Schmitt, Susanne, Müller-Wieland, Dirk, Steiner, Stephan, Heidenreich, Lothar, Offers, Elmar, Gremmler, Uwe, Killat, Holger, Rieker, Werner, Patsilinakos, Sotiris, Kartalis, Athanasios, Manolis, Athanassios, Sionis, Dimitrios, Chachalis, Geargios, Liberopoulos, Evangelos, Skoumas, Ioannis, Athyros, Vasilios, Vardas, Panagiotis, Parthenakis, Frangkiskos, Hahalis, Georgios, Lekakis, John, Hatzitolios, Apostolos, Fausto Ovando, Sergio R, Montenegro Valdovinos, Pablo Carlos, Arango Benecke, Juan L, Rodriguez De Leon, Edgar R, Yan, Bryan PY, Siu, David CW, Turi, Tibor, Merkely, Bela, Gabor Kiss, Robert, Ungi, Imre, Lupkovics, Geza, Nagy, Lajos, Katona, András, Édes, István, Müller, Gábor, Horvath, Iván, Kapin, Tibor, Szigeti, Zsolt, Faluközy, József, Kumbla, Mukund, Sandhu, Manjinder, Annam, Sharath, Reddy Proddutur, Naveen, Regella, Reddy, Premchand, Rajendra K, Mahajan, Ajaykumar, Pawar, Sudhir, Abhyanakar, Atul D, Kerkar, Prafulla, Govinda, Ravishankar A, Oomman, Abraham, Sinha, Dhurjati, Patil, Sachin N, Kahali, Dhiman, Sawhney, Jitendra, Joshi, Abhijeet B, Chaudhary, Sanjeev, Harkut, Pankaj, Guha, Santanu, Porwal, Sanjay, Jujjuru, Srimannarayana, Pothineni, Ramesh B, Monteiro, Minguel R, Khan, Aziz, Iyengar, Shamanna S, Grewal, Jasprakash Singh, Chopda, Manoj, Fulwani, Mahesh C, Patange, Dr. Aparna, Sachin, Patil, Chopra, Vijay K, Goyal, Naresh K, Shinde, Rituparna, Manakshe, Gajendra V, Patki, Nitin, Sethi, Sumeet, Munusamy, Vengatesh, Sunil Thanvi, Sunil Karnaand, Adhyapak, Srilakshmi, Patil, Chandrakant, Pandurangi, Ulhas, Mathur, Rishabh, Gupta, Jugal, Kalashetti, Suhas, Bhagwat, Ajit, Raghuraman, Bagirath, Kumar Yerra, Shiv, Bhansali, Prasant, Borse, Rohidas, Rahul, Patil, Das, Srihari, Kumar, Vinay, Abdullakutty, Jabir, Saathe, Shireesh, Palimkar, Priya, Abdullkutty, Jabir, Sathe, Shireesh, Atar, Shaul, Shechter, Michael, Mosseri, Morris, Arbel, Yaron, Ehud, Chorin, Ofer, Havakuk, Lotan, Chaim, Rosenschein, Uri, Katz, Amos, Henkin, Yaakov, Francis, Adi, Klutstein, Marc, Nikolsky, Eugenia, Zukermann, Robert, Turgeman, Yoav, Halabi, Majdi, Marmor, Alon, Kornowski, Ran, Jonas, Michael, Amir, Offer, Hasin, Yonathan, Rozenman, Yoseph, Fuchs, Shmuel, Zvi, Vered, Hussein, Osamah, Gavish, Dov, Vered, Zvi, Caraco, Yoseph, Elias, Mazen, Tov, Naveh, Wolfovitz, Efrat, Lishner, Michael, Elias, Nizar, Piovaccari, Giancarlo, Pellegrin, Annamaria De, Garbelotto, Raffaella, Guardigli, Gabriele, Marco, Valgimigli, Licciardello, Giovanni, Auguadro, Carla, Scalise, Filippo, Cuccia, Claudio, Salvioni, Alessandro, Musumeci, Giuseppe, Senni, Michelle, Calabrò, Paolo, Novo, Salvatore, Faggiano, Pompilio, Metra, Marco, De Cesare, Nicoletta B, Berti, Sergio, Puccioni, Enrico, Galvani, Marcello, Tespili, Maurizio, Piatti, Piermarco, Palvarini, Michela, Luca, Giuseppe De, Violini, Roberto, Leo, Alessandro De, Olivari, Zoran, Filardi, Pasquale Perrone, Ferratini, Maurizio, Racca, Vittorio, Dai, Kazuoki, Shimatani, Yuji, Kamiya, Haruo, Ando, Kenji, Takeda, Yoshihiro, Morino, Yoshihiro, Hata, Yoshiki, Kimura, Kazuo, Kishi, Koichi, Michishita, Ichiro, Uehara, Hiroki, Higashikata, Toshinori, Hirayama, Atsushi, Hirooka, Keiji, Doi, Yasuji, Sakagami, Satoru, Taguchi, Shuichi, Koike, Akihiro, Fujinaga, Hiroyuki, Koba, Shinji, Kozuma, Ken, Kawasaki, Tomohiro, Ono, Yujiro, Shimizu, Masatoshi, Katsuda, Yousuke, Wada, Atsuyuki, Shinke, Toshiro, Kimura, Takeshi, Ako, Junya, Fujii, Kenshi, Takahashi, Toshiyuki, Nakao, Koichi, Furukawa, Yutaka, Sugino, Hiroshi, Tamura, Ritsu, Mano, Toshiaki, Uematsu, Masaaki, Utsu, Noriaki, Ito, Kashima, Haraguchi, Takuya, Sato, Katsuhiko, Ueda, Yasunori, Nishibe, Akira, Fujimoto, Kazuteru, Masutani, Motomaru, Nishibe, Akira, Fujimoto, Kazuteru, Yoon, Jung Han, Kim, Sang-Hyun, Kim, Hack-Lyoung, Sik Park, Hun, Chae, In-Ho, Kim, Moo Hyun, Jeong, Myung Ho, Rha, Seungwoon, Kim, Chongjin, Kim, Hyo-Soo, Kim, Hae Young, Hong, Taekjong, Tahk, Seung-Jea, Kim, Youngkwon, Busmane, Arija, Pontaga, Natalija, Strelnieks, Aldis, Mintale, Iveta, Sime, Iveta, Petrulioniene, Zaneta, Kavaliauskiene, Roma, Jurgaitiene, Ruta, Sakalyte, Gintare, Slapikas, Rimvydas, Norkiene, Sigute, Misonis, Nerijus, Kibarskis, Aleksandras, Kubilius, Raimondas, Bojovski, Stojko, Kedev, Sasko, Lozance, Nensi, Kjovkaroski, Aleksandar, Doncovska, Snezana, Ong, Tiong Kiam, Kasim, Sazzli, Maskon, Oteh, Kandasamy, Balachandran, Yusoff, Khalid, Liew, Houng B, Wan Mohamed, Wan Mohd Izani, Castillo, Armando García, Ramos López, Gabriel Arturo, Calvillo, Jorge Carrillo, Campos, Pedro Fajardo, Núñez Fragoso, Juan Carlos, Bayram Llamas, Edmundo Alfredo, Alcocer Gamba, Marco Antonio, Madrigal, Jaime Carranza, González Salas, Luis Gerardo, Rosas, Enrique López, González Díaz, Belinda, Vázquez, Eduardo Salcido, Nacoud Ackar, Alfredo, Llamas Esperón, Guillermo Antonio, Martínez Sánchez, Carlos Rodolfo, De Leon, María Guerrero, Suarez Otero, Rodrigo, Salmón, Guillermo Fanghänel, Pérez Ríos, Jesús Antonio, Garza Ruíz, José Angel, Alings, Marco, Breedveld, Robert W, Feenema-Aardema, Margriet, Borger-Van Der Burg, Alida, Hoogslag, Pieter AM, Suryapranata, Harry, Oomen, Antonius, Van Haelst, Paulus, Feenema-Aradema, Margriet, Wiersma, Jacobijne J, Basart, Dirk, Van Der Wal, Ruud MA, Zwart, Peter, Monraats, Pascalle, Van Kesteren, Henricus, Karalis, Ioannis, Jukema, Johan, Verdel, Gerardus JE, Brueren, Bart RG, Troquay, Roland PTh, Viergever, Eric P, Al-Windy, Nadea YY, Bartels, Gerard L, Cornel, Jan H, Hermans, Walter RM, Herrman, Johannes PR, Bos, Robert J, Groutars, Reginald GEJ, Van Der Zwaan, Coenraad C, Kaplan, Refik, Ronner, Eelko, Groenemeijer, Bjorn E, Bronzwaer, Patrick NA, Liem, Anho AH, Rensing, Bernard JWM, Bokern, Marcel JJA, Nijmeijer, Remco, Hersbach, Ferry MRJ, Willems, Frank F, Gosselink, Antonius TM, Rasoul, Saman, Elliott, John, Wilkins, Gerard, Fisher, Raewyn, Scott, Douglas, Hart, Hamish, Stewart, Ralph, Harding, Scott, Ternouth, Ian, Fisher, Nicholas, Wilson, Samuel, Aitken, Denise, Anscombe, Russell, Davidson, Laura, Tomala, Tadeusz, Nygård, Ottar, Sparby, Jon Arne, Andersen, Kjell, Gullestad, Lars, Jortveit, Jarle, Munk, Peter S, Singsaas, Erlend gyllensten, Halvorsen, Sigrun, Hurtig, Ulf, Correa Flores, Roger M, Calderon Ticona, Jorge R, Durand Velasquez, Julio R, Negron Miguel, Sandra A, Sanabria Perez, Enrique S, Carrion Chambilla, Jesus M, Chavez Ayala, Carlos A, Castillo Leon, Reynaldo P, Vargas GonzalesC, Rolando J, Hernandez Zuniga, Jose D, Camacho Cosavalente, Luis A, Bravo Mannucci, Jorge E, Landeo, Javier Heredia, Llerena Navarro, Nassip C, Roldan Concha, Yudy M, Rodriguez Chavez, Víctor E, Anchante Hernandez, Henry A, Zea Nunez, Carlos A, Ramos, Walter Mogrovejo, Ferrolino, Arthur, Sy, Rosa Allyn G, Tirador, Louie, Sy, Rody G., Matiga, Generoso, Coching, Raul Martin, Bernan, Alisa, Rogelio, Gregorio, Morales, Dante D., Tan, Edgar, Sulit, Dennis Jose, Wlodarczak, Adrian, Skonieczny, Grzegorz, Pawlowicz, Lidia, Wojewoda, Pawel, Busz-Papiez, Benita, Bednarski, Janusz, Goch, Aleksander, Staneta, Pawel, Dulak, Elzbieta, Budaj, Andrzej, Saminski, Krzysztof, Krasowski, Wlodzimierz, Sudnik, Wanda, Zurakowski, Aleksander, Skorski, Marcin, Lysek, Roman, Miklaszewicz, Beata, Lipko, Jan Andrzej, Kostarska-Srokosz, Edyta, Piepiorka, Marek, Drzewiecka, Anna, Stasiewski, Arkadiusz, Blicharski, Tomasz, Bystryk, Leszek, Szpajer, Michal, Korol, Marek, Czerski, Tomasz, Gniot, Jacek, Lubinski, Andrzej, Gorny, Jerzy, Franek, Edward, Raczak, Grzegorz, Szwed, Hanna, Monteiro, Pedro, Mesquita Bastos, Jose, Pereira, Helder H, Morais, Joao, Seixo, Filipe, Mendonça, Carlos, Botelho, Ana, Caetano, Francisca, Minescu, Bogdan, Istratoaie, Octavian, Tesloianu, Dan N, Dorobantu, Maria, Cristian, Gabriel, Dumitrescu, Silviu, Podoleanu, Cristian GC, Constantinescu, Mircea CA, Bengus, Cristina M, Militaru, Constantin, Rosu, Doina, Parepa, Irinel R, Matei, Adrian V, Alexandru, Tom M, Malis, Mihaela, Coman, Ioan, Shvarts, Yury, Orlikova, Olga, Kobalava, Zhanna, Barbarash, Olga L, Markov, Valentin, Lyamina, Nadezhda, Gordienko, Alexander, Zrazhevsky, Konstantin, Vishnevsky, Alexander Y, Gurevich, Victor, Stryuk, Raisa, Lomakin, Nikita V, Bokarev, Igor, Khlevchuk, Tatiana, Shalaev, Sergey, Khaisheva, Larisa, Chizhov, Petr, Viktorova, Inna, Osokina, Natalya, Shchekotov, Vladimir, Akatova, Evgenia, Chumakova, Galina, Libov, Igor, Voevoda, Mikhail I, Tretyakova, Tatyana V, Baranov, Evgeny, Shustov, Sergey, Yakushin, Sergey, Gordeev, Ivan, Khasanov, Niiaz, Reshetko, Olga, Sotnikova, Tatiana, Molchanova, Olga, Nikolaev, Konstantin, Gapon, Liudmila, Baranova, Elena, Kosmachova, Elena, Karpov, Yuriy, Karpov, Yuri, Povzun, Anton, Egorova, Liudmila, Tyrenko, Vadim V, Ivanov, Igor G, Ilya, Masterov, Kanorsky, Sergey, Simic, Dragan, Ivanovic, Nikola, Davidovic, Goran, Tasic, Nebojsa, Asanin, Milika R., Stojic, Stevo, Apostolovic, Svetlana R., Ilic, Stevan, Tosic, Biljana Putnikovic, Stankovic, Aleksandar, Arandjelovic, Aleksandra, 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Published
2022
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49. Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases

Author

Garcia-Duran, Alberto, Bordes, Antoine, Usunier, Nicolas, and Grandvalet, Yves

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Learning
Abstract

This paper tackles the problem of endogenous link prediction for Knowledge Base completion. Knowledge Bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature., Comment: 26 pages

Published
2015

50. Knowledge Graph Completion to Predict Polypharmacy Side Effects

Author

Malone, Brandon, García-Durán, Alberto, Niepert, Mathias, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Auer, Sören, editor, and Vidal, Maria-Esther, editor

Published
2019
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