Amazon.com

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.

Convolutional Sequence Generation for Skeleton-Based Action Synthesis

Abstract: In this work, we aim to generate long actions represented as sequences of skeletons. The generated sequences must demonstrate continuous, meaningful human actions, while maintaining coherence among body parts. Instead of generating skeletons sequentially following an autoregressive model, we propose a framework that generates the entire sequence altogether by transforming from a sequence of latent vectors sampled from a Gaussian process (GP). This framework, named Convolutional Sequence Generation Network (CSGN), jointly models structures in temporal and spatial dimensions. It captures the temporal structure at multiple scales through the GP prior and the temporal convolutions; and establishes the spatial connection between the latent vectors and the skeleton graphs via a novel graph refining scheme. It is noteworthy that CSGN allows bidirectional transforms between the latent and the observed spaces, thus enabling semantic manipulation of the action sequences in various forms. We conducted empirical studies on multiple datasets, including a set of high-quality dancing sequences collected by us. The results show that our framework can produce long action sequences that are coherent across time steps and among body parts.

Detecting fraudulent activity by analysis of information requests

Abstract: Techniques are described for use in inhibiting attempts to fraudulently obtain access to confidential information about users. In some situations, the techniques involve automatically analyzing at least some requests for information that are received by a Web site or other electronic information service, such as to determine whether they likely reflect fraudulent activities by the request senders or other parties that initiate the requests. For example, if a request is being made to a Web site based on a user's interaction with a third-party information source (e.g., another unaffiliated Web site) that is not authorized to initiate the request, the third-party information source may be a fraudulent phishing site or engaging in other types of fraudulent activity. If fraudulent activity is suspected based on analysis of one or more information requests, one or more actions may be taken to inhibit the fraudulent activity.

Social networking system

Abstract: A networked computer system provides various services for assisting users in locating, and establishing contact relationships with, other users For example, in one embodiment, users can identify other users based on their affiliations with particular schools or other organizations The system also provides a mechanism for a user to selectively establish contact relationships or connections with other users, and to grant permissions for such other users to view personal information of the user The system may also include features for enabling users to identify contacts of their respective contacts In addition, the system may automatically notify users of personal information updates made by their respective contacts

Point Cloud GAN

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.

Data mining of user activity data to identify related items in an electronic catalog

Abstract: Various methods are disclosed for monitoring user browsing activities that indicate user interests in particular products, or other items, represented in an electronic catalog, and for using such information to identify items that are related to one another. In one embodiment, relationships between items within an electronic catalog are determined by identifying items that are frequently viewed by users within the same browsing session (e.g., items A and B are related because a significant portion of those who viewed A also viewed B). The resulting item relatedness data may be stored in a table that maps items to sets of related items. The table may be used to provide personalized item recommendations to users, and/or to supplement item detail pages of the electronic catalog with lists of related items. In one embodiment, the table is used to provide session-specific item recommendations to users.