Institution
Amazon.com
Company•Seattle, Washington, United States•
About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.
Topics: Computer science, Service (business), Service provider, Context (language use), Virtual machine
Papers published on a yearly basis
Papers
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University of Leeds1, Royal Botanic Garden Edinburgh2, National Autonomous University of Mexico3, James Cook University4, University of Kent5, Universidad del Tolima6, Wageningen University and Research Centre7, National Institute of Amazonian Research8, University of Lorraine9, Paul Sabatier University10, University of Edinburgh11, University of Texas at Austin12, Museu Paraense Emílio Goeldi13, University of Wisconsin–Milwaukee14, Smithsonian Tropical Research Institute15, Amazon.com16, Conservation International17, University College London18, Environmental Change Institute19, Universidade do Estado de Mato Grosso20, National University of Colombia21, Duke University22, Universidad Nacional de la Amazonía Peruana23, University of Los Andes24, Empresa Brasileira de Pesquisa Agropecuária25, Universidade Federal do Acre26, Utrecht University27, Naturalis28
TL;DR: It is shown that fast demographic traits – short turnover times – are associated with high diversification rates across 51 clades of canopy trees, which reveals the crucial role of intrinsic, ecological variation among clades for understanding the origin of the remarkable diversity of Amazonian trees and forests.
Abstract: The Amazon rain forest sustains the world's highest tree diversity, but it remains unclear why some clades of trees are hyperdiverse, whereas others are not. Using dated phylogenies, estimates of current species richness and trait and demographic data from a large network of forest plots, we show that fast demographic traits – short turnover times – are associated with high diversification rates across 51 clades of canopy trees. This relationship is robust to assuming that diversification rates are either constant or decline over time, and occurs in a wide range of Neotropical tree lineages. This finding reveals the crucial role of intrinsic, ecological variation among clades for understanding the origin of the remarkable diversity of Amazonian trees and forests.
69 citations
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TL;DR: Zhang et al. as mentioned in this paper proposed a two fold modification to GAN algorithm for learning to generate point clouds (PC-GAN), which combines ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process.
Abstract: Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to GAN algorithm for learning to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, instead of using only state-of-the-art Wasserstein GAN objective, we propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form. Thereby, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We validate our claims on ModelNet40 benchmark dataset. Using the distance between generated point clouds and true meshes as metric, we find that PC-GAN trained by the sandwiching objective achieves better results on test data than the existing methods. Moreover, as a byproduct, PC- GAN learns versatile latent representations of point clouds, which can achieve competitive performance with other unsupervised learning algorithms on object recognition task. Lastly, we also provide studies on generating unseen classes of objects and transforming image to point cloud, which demonstrates the compelling generalization capability and potentials of PC-GAN.
69 citations
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TL;DR: This work formalizes the concept of dataset bias under the framework of distribution shift and presents a simple debiasing algorithm based on residual fitting, which is called DRiFt, to design learning algorithms that guard against known dataset bias.
Abstract: Statistical natural language inference (NLI) models are susceptible to learning dataset bias: superficial cues that happen to associate with the label on a particular dataset, but are not useful in general, e.g., negation words indicate contradiction. As exposed by several recent challenge datasets, these models perform poorly when such association is absent, e.g., predicting that "I love dogs" contradicts "I don't love cats". Our goal is to design learning algorithms that guard against known dataset bias. We formalize the concept of dataset bias under the framework of distribution shift and present a simple debiasing algorithm based on residual fitting, which we call DRiFt. We first learn a biased model that only uses features that are known to relate to dataset bias. Then, we train a debiased model that fits to the residual of the biased model, focusing on examples that cannot be predicted well by biased features only. We use DRiFt to train three high-performing NLI models on two benchmark datasets, SNLI and MNLI. Our debiased models achieve significant gains over baseline models on two challenge test sets, while maintaining reasonable performance on the original test sets.
68 citations
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29 Dec 2005TL;DR: In this paper, a method and system for determining interest spaces among online content sources is presented, where the representation reflects navigation paths and respective navigation path weights among the online sources, and where the weights are indicative of user activity among the sources.
Abstract: A method and system for determining interest spaces among online content sources. According to one embodiment, a method may include generating a representation of a network of online content sources, where the representation reflects navigation paths and respective navigation path weights among the online content sources, and where the navigation path weights are indicative of user activity among the online content sources. The method may further include generating one or more interest spaces of the network dependent upon the navigation paths and respective navigation path weights, where each interest space includes at least a subset of the online content sources.
68 citations
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TL;DR: In this paper, a tractable empirical model of equilibrium behavior at first-price, sealed-bid auctions is developed within the affi litated private-values paradigm, but the rate of convergence in estimation is slow when the number of bidders is even moderately large, so they develop a semiparametric estimation strategy, focusing on the Archimedean family of copulae.
68 citations
Authors
Showing all 13498 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jiawei Han | 168 | 1233 | 143427 |
Bernhard Schölkopf | 148 | 1092 | 149492 |
Christos Faloutsos | 127 | 789 | 77746 |
Alexander J. Smola | 122 | 434 | 110222 |
Rama Chellappa | 120 | 1031 | 62865 |
William F. Laurance | 118 | 470 | 56464 |
Andrew McCallum | 113 | 472 | 78240 |
Michael J. Black | 112 | 429 | 51810 |
David Heckerman | 109 | 483 | 62668 |
Larry S. Davis | 107 | 693 | 49714 |
Chris M. Wood | 102 | 795 | 43076 |
Pietro Perona | 102 | 414 | 94870 |
Guido W. Imbens | 97 | 352 | 64430 |
W. Bruce Croft | 97 | 426 | 39918 |
Chunhua Shen | 93 | 681 | 37468 |