Institution
Pompeu Fabra University
Education•Barcelona, Spain•
About: Pompeu Fabra University is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 8093 authors who have published 23570 publications receiving 858431 citations. The organization is also known as: Universitat Pompeu Fabra & UPF.
Topics: Population, Context (language use), Gene, Computer science, Politics
Papers published on a yearly basis
Papers
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01 Nov 2020TL;DR: This paper proposes a new evaluation framework (TweetEval) consisting of seven heterogeneous Twitter-specific classification tasks, and shows the effectiveness of starting off with existing pre-trained generic language models, and continue training them on Twitter corpora.
Abstract: The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction. Therefore, it is unclear what the current state of the art is, as there is no standardized evaluation protocol, neither a strong set of baselines trained on such domain-specific data. In this paper, we propose a new evaluation framework (TweetEval) consisting of seven heterogeneous Twitter-specific classification tasks. We also provide a strong set of baselines as starting point, and compare different language modeling pre-training strategies. Our initial experiments show the effectiveness of starting off with existing pre-trained generic language models, and continue training them on Twitter corpora.
328 citations
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TL;DR: A new theoretical framework for RSNs is proposed that can serve as a fertile ground for empirical testing and reflects the dynamical capabilities of the brain, which emphasizes the vital interplay of time and space.
327 citations
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09 Jun 2003TL;DR: This paper introduces a labeling scheme into RTDP that speeds up its convergence while retaining its good anytime behavior, and shows that Labeled RTDP (LRTDP) converges orders of magnitude faster than RTDP, and faster also than another recent heuristic-search DP algorithm, LAO*.
Abstract: RTDP is a recent heuristic-search DP algorithm for solving non-deterministic planning problems with full observability. In relation to other dynamic programming methods, RTDP has two benefits: first, it does not have to evaluate the entire state space in order to deliver an optimal policy, and second, it can often deliver good policies pretty fast. On the other hand, RTDP final convergence is slow. In this paper we introduce a labeling scheme into RTDP that speeds up its convergence while retaining its good anytime behavior. The idea is to label a state s as solved when the heuristic values, and thus, the greedy policy defined by them, have converged over s and the states that can be reached from s with the greedy policy. While due to the presence of cycles, these labels cannot be computed in a recursive, bottom-up fashion in general, we show nonetheless that they can be computed quite fast, and that the overhead is compensated by the recomputations avoided. In addition, when the labeling procedure cannot label a state as solved, it improves the heuristic value of a relevant state. This results in the number of Labeled RTDP trials needed for convergence, unlike the number of RTDP trials, to be bounded. From a practical point of view, Labeled RTDP (LRTDP) converges orders of magnitude faster than RTDP, and faster also than another recent heuristic-search DP algorithm, LAO*. Moreover, LRTDP often converges faster than value iteration, even with the heuristic h = 0, thus suggesting that LRTDP has a quite general scope.
326 citations
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TL;DR: It is shown that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions.
326 citations
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TL;DR: In this paper, an attendance equation is estimated using data on individual games played in the Spanish First Division Football League, including economic variables, quality, uncertainty, and opportunity costs, and the expected effects on attendance for all the variables.
Abstract: An attendance equation is estimated using data on individual games played in the Spanish First Division Football League. The specification includes as explanatory factors: economic variables, quality, uncertainty and opportunity costs. The authors concentrate the analysis on some specification issues such as controlling the effect of unobservables given the panel data structure of the data set, the type of functional form, and the potential endogeneity of prices. The authors obtain the expected effects on attendance for all the variables. The estimated price elasticities are, in general, smaller than one in absolute value but are sensitive to the specification issues, in particular, the endogeneity of prices.
326 citations
Authors
Showing all 8248 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrei Shleifer | 171 | 514 | 271880 |
Paul Elliott | 153 | 773 | 103839 |
Bert Brunekreef | 124 | 806 | 81938 |
Philippe Aghion | 122 | 507 | 73438 |
Anjana Rao | 118 | 337 | 61395 |
Jordi Sunyer | 115 | 798 | 57211 |
Kenneth J. Arrow | 113 | 411 | 111221 |
Xavier Estivill | 110 | 673 | 59568 |
Roderic Guigó | 108 | 304 | 106914 |
Mark J. Nieuwenhuijsen | 107 | 647 | 49080 |
Jordi Alonso | 107 | 523 | 64058 |
Alfonso Valencia | 106 | 542 | 55192 |
Luis Serrano | 105 | 452 | 42515 |
Vadim N. Gladyshev | 102 | 490 | 34148 |
Josep M. Antó | 100 | 493 | 38663 |