Petuum Inc.

Microsoft Technology Licensing, LLC

Opro.ai Inc.

Pengtao Xie

Eric Xing

Ran Gilad-Bachrach

Mikhail Bilenko

Thomas William Finley

Qirong Ho

Roey Flor

Dian Li

Hongbao Zhang

Adam Schwab

The current disclosure is directed towards system and method for controlling industrial process. In one example, a method comprising deploying a forecast model for controlling an industrial process with training configurations that can be used as a single point of truth for guiding training and retraining versions of the forecast model using a model training algorithm without human input. The retraining and redeployment of the forecast model may be triggered when the performance of the forecast model degrades.

Autonomous industrial process control system and method that provides autonomous retraining of forecast model

A computer in a distributed peer-to-peer system is disclosed. The distributed system includes a plurality of computers configured to run a distributed machine learning (ML) program represented as an expression of a target loss function with a model parameter matrix. The computer includes: a parser module configured to convert a loss function in the distributed program into an expression graph and then one or more multiplication trees; a parameter replica module in communication with the parser module, the parameter replica module configured to maintain the model parameter matrix of the ML program; a compressor module in communication with the parameter replica module, the compressor module configured to extract sufficient factors from the expression graph for updating the model matrix; and a communication module in communication with the compressor module, the communication module configured to send the sufficient factors for updating model matrix to other machines in the distributed system.

Efficient peer-to-peer architecture for distributed machine learning

A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.

Systems and methods for predicting medications to prescribe to a patient based on machine learning

A system for assigning concepts to a medical image includes a visual feature module and a tagging module. The visual feature module is configured to obtain an image feature vector from the medical image. The tagging module is configured to apply a machine-learned algorithm to the image feature vector to assign a set of concepts to the image. The system may also include a text report generator that is configured to generate a written report describing the medical image based on the set of concepts assigned to the medical image.

Systems and methods for automatically tagging concepts to, and generating text reports for, medical images based on machine learning

A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.

Constituent centric architecture for reading comprehension

Embodiments described herein are directed to methods and systems for performing neural network computations on encrypted data. Encrypted data is received from a user. The encrypted data is encrypted with an encryption scheme that allows for computations on the ciphertext to generate encrypted results data. Neural network computations are performed on the encrypted data, using approximations of neural network functions to generate encrypted neural network results data from encrypted data. The approximations of neural network functions can approximate activation functions, where the activation functions are approximated using polynomial expressions. The encrypted neural network results data are communicated to the user associated with the encrypted data such that the user decrypts the encrypted data based on the encryption scheme. The functionality of the neural network system can be provided using a cloud computing platform that supports restricted access to particular neural networks.

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Pittsburgh, PA, United States of America

Pengtao Xie