Amazon Technologies, Inc.

A9.com, Inc.

Mitek Systems, Inc.

Oleg Rybakov

Matias Omar Gregorio Benitez

Rahul Bhotika

Rejith George Joseph

Avinash Aghoram Ravichandran

Junxiong Jia

Leo Parker Dirac

Chang Yuan

Vijai Mohan

Rakesh Madhavan Nambiar

Christopher John Lish

Geoffrey Heller

Jim Oommen Thomas

John J Roach

Scott Daniel Helmer

Lixin Duan

Siddharth Singh

Kevin Andrew Bell

Anshul Kumar Jain

Jian Dong

Grigori Nepomniachtchi

Srikanth Thirumalai

Yue Liu

Daniel Bibireata

Sharadh Ramaswamy

Nikko Strom

Avnish Sikka

Vivek A Shah

Sri Venkata Surya Siva Rama Krishna Garimella

Sonjeev Jahagirdar

Ajay Kumar Mishra

Robert L Couch

Wei Zhang

Neil David Cooper

Michael Gillen

Che-Chun Su

Sree Hari Krishnan Parthasarathi

Ram Prasad Venkatesan

Fnu Vishnu Narayanan

Pranav Prashant Ladkat

Andre Young Moeller

Exemplary embodiments provide distributed parallel training of a machine learning model. Multiple processors may be used to train a machine learning model to reduce training time. To synchronize trained model data between the processors, data is communicated between the processors after some number of training cycles. To improve the communication efficiency, exemplary embodiments synchronize data among a set of processors after a predetermined number of training cycles, and synchronize data between one or more processors of each set of the processors after a predetermined number of training cycles. During the first synchronization among a set of processors, compressed model gradient data generated after performing the training cycles may be communicated. During the second synchronization between the set of processors, trained models or full model gradient data generated after performing the training cycles may be communicated.

Distributed model training

Real-time evaluation and enhancement of image quality prior to capturing an image of a document on a mobile device is provided. An image capture process is initiated on a mobile device during which a user of the mobile device prepares to capture the image of the document, utilizing hardware and software on the mobile device to measure and achieve optimal parameters for image capture. Feedback may be provided to a user of the mobile device to instruct the user on how to manually optimize certain parameters relating to image quality, such as the angle, motion and distance of the mobile device from the document. When the optimal parameters for image capture of the document are achieved, at least one image of the document is automatically captured by the mobile device.

Systems and methods for automatic image capture on a mobile device

Various approaches discussed herein enable browsing groups of visually similar items to an item of interest, wherein the item of interest may be identified in a query image, for example. One or more visual attributes associated with the item of interest are identified, and the visually similar items matching at least one of the visual attributes are grouped together, wherein the group is ranked according to the visually similar items' overall visual similarity to the item of interest, for example by using a visual similarity score and/or metric.

Image similarity-based group browsing

The present disclosure is directed to generating neural network (NN) output using input data representing various types of events, such as input representing a certain type of event and also an engagement metric that may be representative of a property of the event or representative of a related but different type of event. For example, the output values generated using the NN may be associated with the likelihood that certain future events will occur, given the occurrence of certain past or current events. The output can then be modified (e.g., re-ranked, adjusted, etc.) based on the occurrence of certain other past or current events.

Neural network with re-ranking using engagement metrics

The present disclosure is directed to reducing model size of a machine learning model with encoding. The input to a machine learning model may be encoded using a probabilistic data structure with a plurality of mapping functions into a lower dimensional space. Encoding the input to the machine learning model results in a compact machine learning model with a reduced model size. The compact machine learning model can output an encoded representation of a higher-dimensional space. Use of such a machine learning model can include decoding the output of the machine learning model into the higher dimensional space of the non-encoded input.

Encodings for reversible sparse dimensionality reduction

Some aspects of the present disclosure relate to generating and training a neural network by separating historical item interaction data into both inputs and outputs. This may be done, for example, based on date. For example, a neural network machine learning technique may be used to generate a prediction model using a set of inputs that includes both a number of items purchased by a number of users before a certain date as well as some or all attributes of those items, and a set of outputs that includes the items purchased after that date. The items purchased before that date and the associated attributes can be subjected to a time-decay function.

Recommendation system using improved neural network

The present disclosure is directed to training, and providing recommendations via, a temporal ensemble of neural networks. The neural networks in the temporal ensemble can be trained at different times. For example, a neural network can be periodically trained using current item interaction data, for example once per day using purchase histories of users of an electronic commerce system. The item interaction data can be split into a more recent group and a less recent group, for example the last two weeks of data and the remainder of the last two years of data. The periodic training of neural networks, using updated data and the sliding windows created by the date split, results in a number of different models for predicting item interaction events. Using a collection of these neural networks together as a temporal ensemble can increase recommendation accuracy without requiring additional hardware for training.

Temporal ensemble of machine learning models trained during different time intervals

Techniques described herein are directed to improved artificial neural network machine learning techniques that may be employed with a recommendation system to provide predictions with improved accuracy. In some embodiments, item consumption events may be identified for a plurality of users. From these item consumption events, a set of inputs and a set of outputs may be generated according to a data split. In some embodiments, the set of outputs (and potentially the set of inputs) may include item consumption events that are weighted according to a time-decay function. Once a set of inputs and a set of outputs are identified, they may be used to train a prediction model using an artificial neural network. The prediction model may then be used to identify predictions for a specific user based on user-specific item consumption event data.

Approaches introduce a pre-processing and post-processing framework to a neural network-based approach to identify items represented in an image. For example, a classifier that is trained on several categories can be provided. An image that includes a representation of an item of interest is obtained. Rotated versions of the image are generated and each of a subset of the rotated images is analyzed to determine a probability that a respective image includes an instance of a particular category. The probabilities can be used to determine a probability distribution of output category data, and the data can be analyzed to select an image of the rotated versions of the image. Thereafter, a categorization tree can then be utilized, whereby for the item of interest represented the image, the category of the item can be determined. The determined category can be provided to an item retrieval algorithm to determine primary content for the item of interest. This information also can be used to determine recommendations, advertising, or other supplemental content, within a specific category, to be displayed with the primary content.

Object recognition

Various approaches discussed herein enable browsing groups of visually similar items to an item of interest, wherein the item of interest may be identified in a query image, for example. One ore more visual attributes associated with the item of interest are identified, and the visually similar items matching at least one of the visual attributes are grouped together, wherein the group is ranked according to the visually similar items' overall visual similarity to the item of interest, for example by using a visual similarity score and/or metric.

Disclosed are various embodiments for comparing images of network pages using computer vision to identify changes that have occurred between two versions of a network page. A first plurality of segments in a first image representing a first version of a network page are identified and a second plurality of segments in a second image representing a second version of the network page are identified. It is then determined whether each segment in the first plurality of segments matches a respective segment in the second plurality of segments or vice versa.

Image comparison for user interface testing

Recommendations can be generated even in situations where sufficient user information is unavailable for providing personalized recommendations. Instead of generating recommendations for an item based on item type or category, a relation graph can be consulted that enables other items to be recommended that are related to the item in some way, which may be independent of the type or category of item. For example, images of models, celebrities, or everyday people wearing items of clothing, jewelry, handbags, shoes, and other such items can be received and analyzed to recognize those items and cause them to be linked in the relation graph. When generating recommendations or selecting advertisements, the relation graph can be consulted to recommend products that other people have obtained with the item from any of a number of sources, such that the recommendations may be more valuable to the user.

Object relation builder

A computing device can be configured to analyze information, such as frames captured in a video by a camera in the computing device, to determine locations of objects in captured frames using a scene-based tracking approach without individually having to track the identified objects across the captured frames. The computing device can track scenes, a global planar surface, across newly captured frames and the changes to (or transformation) the scene can be used to determine updated locations for objects that were identified in previously captured frames. Changes to the scene between frames can be measured using various techniques for estimating homographies. An updated location for the particular object in the currently captured frame can be determined by adjusting the location of the object, as determined in the previously captured frame, with respect to the transformation of the scene between the previously captured frame and the currently captured frame.

Approaches for scene-based object tracking

Growing community of inventors

Seattle, WA, United States of America

Oleg Rybakov