Amazon Technologies, Inc.

Gm Global Technolgoy Operations, Inc.

Vineet Khare

Gurumurthy Swaminathan

Xiong Zhou

Ragav Venkatesan

Runfei Luo

Leo Parker Dirac

Saurabh Gupta

Alexander Johannes Smola

Craig Wiley

Bharathan Balaji

Sahika Genc

Rahul G Chougule

Sunil Mallya Kasaragod

Satnam Singh

Dnyanesh Rajpathak

Amit Arora

Asim Tewari

Srikar Appalaraju

Vineet Shashikant Chaoji

Gourav Roy

Saurabh Sohoney

Sugato Chakrabarty

Aswin Natarajan

Orchid Majumder

Amol Wanjari

Urvashi Chowdhary

Andrew Dennis Willingham

Vipul Bhargava

Yijie Zhuang

Pramuditha Hemanga Perera

Sivaramakrishnan Kaveri

Ashish Hari Chiplunkar

Chellappan Lakshmanan

Siddhartha Agarwal

Compression profiles may be searched for trained neural networks. An iterative compression profile search may be performed response to a search request. Different prospective compression profiles may be generated for trained neural networks according to a search policy. Performance of compressed versions of the trained neural networks according to the compression profiles may be tracked. The search policy may be updated according to an evaluation of the performance of the compression profiles for the compressed versions of the trained neural networks using compression performance criteria. When a search criteria is satisfied, a result for the compression profile search may be provided.

Searching compression profiles for trained neural networks

A machine learning environment utilizing training data generated by customer networks. A reinforcement learning machine learning environment receives and processes training data generated by simulated hosted, or integrated, customer networks. The reinforcement learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the integrated customer networks. The customer networks include an agent process that collects training data and forwards the training data to the machine learning clusters. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configure the application of the reinforcement learning machine learning processes.

Integrated machine learning training

Techniques for providing and servicing listed repository items such as algorithms, data, models, pipelines, and/or notebooks are described. In some examples, web services provider receives a request for a listed repository item from a requester, the request indicating at least a category of the repository item and each listing of a repository item includes an indication of a category that the listed repository item belongs to and a storage location of the listed repository item, determines a suggestion of at least one listed repository item based on the request, and provides the suggestion of the at least one listed repository item to the requester.

Machine learning repository service

A machine learning environment utilizing training data generated by customer environments. A reinforced learning machine learning environment receives and processes training data generated by independently hosted, or decoupled, customer environments. The reinforced learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the decoupled customer environments. The customer environments include an agent process that collects training data and forwards the training data to the machine learning clusters without exposing the customer environment. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configured the application of the reinforced learning machine learning processes.

Decoupled machine learning training

Neural networks with similar architectures may be compressed using shared compression profiles. A request to compress a trained neural network may be received and an architecture of the neural network identified. The identified architecture may be compared with the different network architectures mapped to compression profiles to select a compression profile for the neural network. The compression profile may be applied to remove features of the neural network to generate a compressed version of the neural network.

Applying compression profiles across similar neural network architectures

Techniques for sequence to sequence (S2S) model building and/or optimization are described. For example, a method of receiving a request to build a sequence to sequence (S2S) model for a use case, wherein the request includes at least a training data set, generating parts of a S2S algorithm based on the at least one use case, determined parameters, and determined hyperparameters, and training a S2S algorithm built from the parts of the S2S algorithm using the training data set to generate the S2S model is detailed.

Building sequence to sequence (S2S) models using previously generated S2S models with similar use cases

Techniques for iterative model training and deployment for automated learning systems are described. A method of iterative model training and deployment for automated learning systems comprises generating training data based on inference data, provided by a first version of a model hosted at an endpoint of a machine learning service, and feedback data, received from a client application, using an identifier associated with the inference data and the feedback data, generating a second version of the model using the training data, and deploying the model to the endpoint of the machine learning service.

Iterative model training and deployment for automated learning systems

A compression policy to produce compression profiles for compressing trained machine learning models may be trained using reinforcement learning. An iterative reinforcement learning may be performed response to a search request. Different prospective compression profiles may be generated for received machine learning models according to a compression policy being trained. Performance of compressed versions of the trained neural networks according to the compression profiles may be caused using data sets used to train the machine learning models. The compression policy may be updated according to reward signal determined from an application of a reward function for performance criteria to performance results of the different versions of the machine learning models. When a search criteria is satisfied, the trained compression policy may be provided.

Reinforcement learning for training compression policies for machine learning models

Features related to systems and methods expediting generation of a machine learning model, such as an image recognition model, are described. Existing machine learning models are analyzed to identify a starting point for creating the new machine learning model. An existing machine learning model can suggest learning parameters (e.g., training parameters or structural features of the model) that can be used to expedite the generating and training process along with training data that can augment the training of the new machine learning model.

Generating artificial intelligence image processing services

A method and system for a deep learning-based approach to image processing to increase a level of optical zooming and increasing the resolution associated with a captured image. The system includes an image capture device to generate a display of a field of view (e.g., of a scene within a viewable range of a lens of the image capture device). An indication of a desired zoom level (e.g., 1.1&#xd7; to 5&#xd7;) is received, and, based on this selection, a portion of the field of view is cropped. In one embodiment, the cropped portion displayed by the image capture device for a user's inspection, prior to the capturing of a low resolution image. The low resolution image is provided to an artificial neural network trained to apply a resolution up-scaling model to transform the low resolution image to a high resolution image of the cropped portion.

Neural network-based image processing

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

Techniques are disclosed herein for reactively identifying software products, available from an electronic marketplace, that are exhibiting anomalous behavior. Data associated with software products is accessed and analyzed to determine anomalous behavior. The data analyzed may include, but is not limited to, crash data, ratings data, marketplace data, usage data, and the like. A machine learning mechanism may be used to classify the application into a category relating to whether a potential anomaly is identified for the software product. A score may also be calculated for the software applications that indicates a severity of the anomalous behavior. The classification and/or the score may be used to determine whether to perform further analysis or testing with regard to a software product. For instance, the score may be used to determine that the software product is to be tested by a testing service.

Reactively identifying software products exhibiting anomalous behavior

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Redmond, WA, United States of America

Vineet Khare