Amazon Technologies, Inc.

Pooja Ashok Kumar

Sumit Negi

Yashal Shakti Kanungo

Shilpa Pundi Ananth

Brittney C Gannon

Lauren K Johnson

Sai Sree Harsha

Rajeev Ramnarain Rastogi

Naveen Sudhakaran Nair

Neela Sawant

Roopesh Ranjan

Devices, systems, and methods are provided for generating and selecting video clips for inclusion in video sequences based on still frame images. A method may include encoding first embeddings for a first video including first images of an item at a first scene, the first embeddings indicative of features of the first scene; encoding second embeddings for a second video including second images of the item at a second scene, the second embeddings indicative of features of the second scene; encoding third embeddings for the first video, the third embeddings indicative of features of a first type of camera shot used for the first images; encoding fourth embeddings for the second video, the fourth embeddings indicative of features of a second type of camera shot used for the second images; and generating, based on the first, second, third, and fourth embeddings, a video sequence for the item.

Enhanced generation and selection of video clips from still frame images

An approximate count of a subset of records of a data set is obtained using one or more transformation functions. The subset comprises records which contain a first value of one input variable, a second value of another input variable, and a particular value of a target variable. Using the approximate count, an approximate correlation metric for a multidimensional feature and the target variable is obtained. Based on the correlation metric, the multidimensional feature is included in a candidate feature set to be used to train a machine learning model.

Scalable generation of multidimensional features for machine learning

Systems and methods are provided for determining or predicting user demographic information using user behaviors through a Bayesian inference. A computing system may determine demographic information (such as age or gender) of a user based on a Bayesian update and a purchase or other user action by the user. In some embodiments, the computing system may determine the household composition of a user account based on multiple purchases by the user account. The computing system may generate recommendations for the user or the user account based on the demographic information of the user or the household composition of the user account.

Inferring user demographics from user behavior using Bayesian inference

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Bengaluru, India

Pooja Ashok Kumar