Robert Boschgmbh

Amazon Technologies, Inc.

Muhammad Bilal Zafar

Martin Schiegg

Krishnaram Kenthapadi

Stefan Angermaier

Michele Donini

Pinar Altin Yilmaz

Xinyu Liu

Rishita Rajal Anubhai

Cedric Philippe Archambeau

Melih Kandemir

Tyler Stephen Hill

Sebastian Gerwinn

Sanjiv Ranjan Das

Lin Lee Cheong

Roman Dominik Kilgus

Huzefa Rangwala

Ashish Rajendra Rathi

Pedro L Larroy

Keerthan Harish Vasist

Michaela Hardt

Haibo Ding

Buote Xu

Techniques for implementing and using a customizable framework for natural language processing explainability are described. A user of a machine learning (ML) service can generate an explainability configuration identifying a user-selected segmented or a user-selected granularity level, from multiple granularity levels supported by the ML service, at which to use for segmenting text during an ML explainability analysis. The ML service can execute a user-configurable explanation pipeline as part of the explainability analysis of at least an input text, including segmenting the input text using the user-selected segmented or a segmentation algorithm corresponding to the user-selected granularity level to yield candidate segments, generating a mask corresponding to the candidate segments, and executing an explanation algorithm based at least on use of the candidate segments and the mask to yield a result.

Customizable framework for natural language processing explainability

A machine learning system and method of operating a machine learning system for determining a time series, comprising providing an input for a first in particular generative model depending on a probabilistic variable, determining an output of the first model in response to the input for the first model, the output of the first model characterizing the time series. The first model comprises a first layer that is trained to map input for the first model determined depending on the probabilistic variable to output characterizing intermediate data, and a second layer that is trained to map the intermediate data to the time series depending on an output of a third layer of the first model. The output of the third layer characterizes a physical constraint to a machine state. Values of the time series or of the intermediate data are constrained by the output of the third layer.

Computer implemented machine learning system and a method for operating the machine learning system for determining a time series

A method for determining a state of a transmission for a vehicle includes providing an input for a first generative model depending on a route information, a vehicle speed, a probabilistic variable, and an output of a second physical model, and determining an output of the first model characterizing the state of the transmission in response to the input for the first model. The first model includes a first layer trained to map input to an intermediate state and a second layer trained to map the intermediate state to the state of the transmission depending on the output of the second model. The method includes providing an input for the second physical model depending on at least one vehicle state and/or the route information, and determining an output of the second model in response to the input for the second model. The output of the second model characterizes limit(s) for the intermediate state.

Device, method and machine learning system for determining a state of a transmission for a vehicle

Explanation jobs may be performed for computer vision tasks. A request to execute an explanation job for a computer vision machine learning model may be received. The execution job may be performed, including extracting different features from the image, determining the respective relative importance values of the different features on inferences generated by the computer vision machine learning model. The result of the explanation job, including the generated heat maps may be provided.

Performing explanation jobs for computer vision tasks

A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

Global explanations of machine learning model predictions for input containing text attributes

A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.

Method for ascertaining driving profiles

A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

Growing community of inventors

Berlin, Germany

Muhammad Bilal Zafar