Airbnb, Inc.

Sony Corporation

Waymo LLC

Peng Ye

Xun Xu

Li Zhang

Yangli Hector Yee

Spencer De Mars

Kim Pham

Benjamin Henry Yolken

Fenglin Liao

Julian Qian

Siarhei Bykau

Samuel William Lenius

Kelvin Kwong

Robert J Lockwood

Zhe Li

Fan Yang

William Martin Peters, Jr

Chia-Hung Lin

Craig Moriwaki

Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.

Cross-listed property matching using image descriptor features

A rotary joint includes a shaft having a first end, a second end, and a cavity. The rotary joint includes a first waveguide section having a first proximal end and a first distal end. The first proximal end of the first waveguide section is positioned within the cavity and secured to the inner surface of the shaft. The rotary joint includes a second waveguide section that includes a second proximal end and a second distal end. The second proximal end of the second waveguide section is positioned within the cavity of the shaft and unsecured to the inner surface of the shaft to form a radial gap between an outer surface of the second proximal end and a laterally adjacent portion of the inner surface of the shaft. The shaft and the first waveguide section are configured to rotate about the rotational axis and relative to the second waveguide section.

Broadband rotary joint for millimeter wave transmission

Systems and methods are provided for extracting a plurality of features for a listing from a datastore comprising a plurality of listings and a plurality of features for each of the plurality of listings, determining a cluster of similar listings to the listing and generating a set of cluster features for the cluster of similar listings, analyzing the set of cluster features for the cluster of similar listings based on a booking price, using a first trained machine learning model to determine a cluster-level probability of booking the listing on the given date, analyzing the plurality of features for the listing using the booking price, using a second trained machine learning model to determine a listing-level probability of booking the listing on the given date, and generating a final probability of booking by combining the cluster-level probability of booking and the listing-level probability of booking.

Hierarchical generation of booking probability

This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.

Regression-tree compressed feature vector machine for time-expiring inventory utilization prediction

Various aspects of a system and method to process video based on quantization parameters are disclosed herein. In an embodiment, the method includes extraction of a plurality of features to capture texture information of an image block. A neural network regressor is trained to map the extracted plurality of features to determine an optimal quantization parameter. The image block is encoded by use of the determined optimal quantization parameter.

System and method for video processing based on quantization parameter

A system and method for facilitating inhibiting banding in video data, in part by anticipating when banding may occur given certain encoder parameters. An example method includes receiving an input stream of video data; extracting feature information characterizing the stream of video data; using a feed-forward neural network to process the feature information to estimate when a particular block of the stream of video data will exhibit an artifact when encoded using certain Quantization Parameters (QPs); incorporating the indication into metadata associated with the stream of video data; and transferring the stream of video data and metadata to a video encoder. In a more specific embodiment, the feature information includes color information and texture information, and the neural network includes a feed-forward neural network that includes a classifier with a sigmoid activation function, and which has been trained using a cross-entropy cost function.

Banding prediction for video encoding

Embodiments generally relate to providing systems and methods for assessing image quality of a distorted image relative to a reference image. In one embodiment, the system comprises a convolutional neural network that accepts as an input the distorted image and the reference image, and provides as an output a metric of image quality. In another embodiment, the method comprises inputting the distorted image and the reference image to a convolutional neural network configured to process the distorted image and the reference image and provide as an output a metric of image quality.

Full reference image quality assessment based on convolutional neural network

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Foster City, CA, United States of America

Peng Ye