Amazon Technologies, Inc.

Vineet Shashikant Chaoji

Pranav Garg

Rajeev Ramnarain Rastogi

Arijit Biswas

Ankit Gandhi

Saurabh Sohoney

Karthik Sundaresan Gurumoorthy

Anil Raghavendrachar Yelundur

Vineet Khare

Srikar Appalaraju

Gourav Roy

Aswin Natarajan

Andrew Dennis Willingham

Sivaramakrishnan Kaveri

Seyit Ismail Parsa

Dinesh Mandalapu

Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.

Artificial intelligence system employing graph convolutional networks for analyzing multi-entity-type multi-relational data

Disclosed are various embodiments for automatically identifying whether applications are static or dynamic. In one embodiment, code of an application is analyzed to determine instances of requesting data via a network in the application. Characteristics of the instances of requesting data via the network are provided to a machine learning model. The application is automatically classified as either dynamic or static according to the machine learning model.

Automatically identifying dynamic applications

Systems and methods are disclosed to implement an item metric prediction system that predicts a metric for an item using a feature-based model built using other similar items. In embodiments, the system is used to predict item influence values (IIVs) of items indicating an expected amount of subsequent transactions that is caused by an initial transaction of the items. In embodiments, a sample of item transaction data is distributed to a plurality of task nodes, which execute in parallel to determine the items' observed IIVs from the transaction data. Subsequently, a new IIV is determined for an item whose observed IIV has a low confidence level. A set of similar items is selected, and a set of parameters of a feature-based model are tuned to fit the model to the observed IIVs of the similar items. A new IIV having a high confidence level is then obtained using the model.

Scalable product influence prediction using feature smoothing

At an artificial intelligence based service to detect violations of resource usage policies, an indication of a first data set comprising a plurality of network traffic flow records associated with at least a first device of a set of devices may be obtained. Using the first data set, a machine learning model may be trained to predict whether resource usage of a particular device of a particular network violates a first resource usage acceptability criterion. In response to determining, using a trained version of the model, that the probability that a second device has violated the acceptability criterion exceeds a threshold, one or more actions responsive to the violation may be initiated.

Artificial intelligence system for network traffic flow based detection of service usage policy violations

Methods, systems, and computer-readable media for accurately estimating causal effects for related events are disclosed. A plurality of estimates of causal effects of events are determined. The estimates are determined independently. A subset of the estimates are determined not to satisfy a relationship among the causal effects. A set of accurate estimates are generated based at least in part on the subset of the estimates. The accurate estimates are generated using a smoothing process, and the accurate estimates satisfy the relationship.

Accurately estimating causal effects for related events

At an artificial intelligence system, a neural network model is trained iteratively to generate similarity scores for image pairs. The model includes a first subnetwork with a first number of convolution layers, and a second subnetwork with a different number of convolution layers. A given training iteration includes determining, using a version of the model generated in an earlier iteration, similarity scores for a set of image pairs, and then selecting a subset of the pairs based on the similarity scores. The selected subset is used to train a subsequent version of the model. After the model is trained, it may be used to generate similarity scores for other image pairs, and responsive operations may be initiated if the scores meet a criterion.

Artificial intelligence system for image similarity analysis using optimized image pair selection and multi-scale convolutional neural networks

Features are provided for the analysis of collections of data and automatic grouping of data having certain similarities. A collection of data regarding user interactions with item-specific content can be analyzed. The analysis can be used to identify groups of items that are of interest to groups of similar users and/or to identify groups of users with demonstrated interests in groups of similar items. Data may be analyzed in a 'bottom-up' manner in which correlations within the data are discovered in an iterative manner, or in a 'top-down' manner in which desired top-level groups are specified at the beginning of the process. A bottom-up process may also be distributed among multiple devices or processors to more efficiently discover groups when using large collections of data.

Distributed grouping of large-scale data sets

This disclosure is directed to the automated generation of Machine Learning (ML) models. The system receives a user directive containing one or more requirements for building the ML model. The system further identifies common requirements between the user directive and one or more prior user directives and associates characteristics of the prior user directive, or model generated therefrom, with the user directive. The system further associates performance values generated by continuous monitoring of deployed ML models to individual characteristics of the user directive used to generate each of the deployed ML models. The system continuously improves model generation efficiency, model performance, and first run performance of individual ML models by learning from the improvements made to one or more prior ML models having similar characteristics.

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Bengaluru, India

Vineet Shashikant Chaoji