Robert Boschgmbh

Oath Inc.

Yahoo Assets LLC

Technion Research & Development Foundation Limited

Verizon Media Inc.

Dotan Di Castro

Oren Spector

Liane Lewin-Eytan

Iftah Gamzu

Irena Grabovitch-Zuyev

Noa Avigdor-Elgrabli

Abhinav Pundir

Vladimir Tchuiev

Yoelle Smadja Maarek

Eyal Zohar

Shahar Kvatinsky

Ran Wolff

Doug Sharp

Zohar Karnin

Zohar Feldman

Christian Daniel

Avinoam Kolodny

Umang Patel

Guy Halawi

Daniel Soudry

Maksym Lefarov

Ian McCarthy

Michael W Albers

Joel Oren

Irena Grabovitch

Asaf Gal

Eitan Kosman

Ayal Taitler

Nima Manafzadeh Dizbin

A method for controlling a robot to perform a task. The method includes acquiring, for each target of a sequence of targets comprising at least one intermediate target of the task and a final target of a task, a target image data element comprising at least one target image from a perspective of an end-effector of the robot at a respective target position of the robot and successively according to the sequence of targets, for each target in the sequence, acquiring, for the target, an origin image data element, supplying the origin image data element and the target image data to a machine learning model configured to derive a delta movement between the origin current position and the target position and controlling the robot to move according to the delta movement.

Device and control method using machine learning for a robot to perform an insertion task

A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.

Device and method for scheduling a set of jobs for a plurality of machines

A method for training a neural network to derive, from a force and a moment exerted on an object when pressed on a plane in which an insertion for inserting the object is located, a movement vector to insert an object into an insertion. The method includes, for a plurality of positions in which the object or the part of the object held by the robot touches a plane in which the insertion is located, controlling the robot to move to the position, controlling the robot to press the object onto the plane, measuring the force and moment experienced by the object, scaling the pair of force and moment by a number randomly chosen between zero and a predetermined positive maximum number and labelling the scaled pair by a movement vector between the position and the insertion, and training the neural network using the labelled pairs of force and moment.

Device and method for training a neural network for controlling a robot for an inserting task

A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector for the robot to insert an object into an insertion. The method includes controlling the robot to hold the object, bringing the robot into a target position in which the object is inserted in the insertion, for a plurality of positions different from the target position controlling the robot to move away from the target position to the position, taking a camera image by the camera and labelling the camera image by a movement vector to move back from the position to the target position and training the neural network using the labelled camera images.

A method for controlling a robot to perform a task. The method includes acquiring a target image data element comprising at least one target image from a perspective of an end-effector of the robot at a target position of the robot in which the robot has performed the task, acquiring an origin image data element comprising at least one origin image from the perspective of the end-effector of the robot at an origin position of the robot, supplying the origin image data element and the target image data element to a machine learning model configured to derive a delta movement between the origin current position and the target position and controlling the robot to move according to the delta movement to perform the task.

Device and method for controlling a robot to perform a task

A method for lossily compressing a sequence of video frames into a representation, wherein each video frame comprises pixels that carry color values. The method includes: segmenting each video frame into superpixels, wherein these superpixels are groups of pixels that share at least one predetermined common property; assigning, to each superpixel in each video frame, at least one attribute derived from the pixels belonging to the respective superpixel; and combining superpixels as nodes in a graph representation, wherein superpixels in a same video frame are connected by spatial edges associated with at least one quantity that is a measure for a distance between these superpixels; and in response to superpixels in adjacent video frames in the sequence meeting at least one predetermined relatedness criterion, these superpixels are connected by temporal edges.

Lossy compression of video content into a graph representation

A method for training a machine learning model to derive a movement vector for a robot from image data. The method includes acquiring images from a perspective of an end-effector of the robot, forming training image data elements from the acquired images, generating augmentations of the training image data elements, training an encoder network using contrastive loss and training a neural network to reduce a loss between movement vectors output by the neural network in response to embedding outputs provided by the encoder network and respective ground truth movement vectors.

Device and method for training a machine learning model to derive a movement vector for a robot from image data

Disclosed are systems and methods for improving interactions with and between computers in electronic messaging, and other, systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically generating data extraction rules, which can then be used to automatically extract data from electronic messages.

Automatic electronic message content extraction method and apparatus

A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector to insert an object into an insertion. The method includes, for a plurality of positions in which the object held by the robot touches a plane in which the insertion is located controlling the robot to move to the position, taking a camera image by the camera and labelling the camera image with a movement vector between the position and the insertion in the plane and training the neural network using the labelled camera images.

Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for partitioning HTML content in electronic messages based on the relative positions of the content's links within the DOM hierarchy of the messages, and basing the propagation (e.g., display or communication) of such content therefrom. The disclosed message partitioning and extraction framework can be applied online, in real-time, at scale, without any pre-processing or pre-learning/training.

Computerized system and method for digital content extraction and propagation in HTML messages

Methods, systems, and computer-readable media for anonymizing electronic documents. In accordance with one or more embodiments, structurally-similar electronic documents can be identified among a group of electronic documents (e.g., e-mail messages, documents containing HTML formatting, etc.). A hash function can be specifically tailored to identify the similarly structured documents. The structurally-similar electronic documents can be grouped into a same equivalence class. Masked anonymized document samples can be generated from the structurally-similar electronic documents utilizing the same equivalence class, thereby ensuring that the anonymized document samples when viewed as a part of an audit remain anonymous. An online process is provided to guarantee k-anonymity of the users over the entire lifetime of the auditing process. An auditor's productivity can be measured based on the amount of content revealed to the auditor within the samples he is assigned. The auditor's productivity is maximized while ensuring anonymization over the lifetime of the audit.

Enforcing anonymity in the auditing of electronic documents

As is disclosed herein, user behavior in connection with a number of electronic messages, such as electronic mail (email) messages, can be used to automatically learn from, and predict, whether a message is wanted or unwanted by the user, where an unwanted message is referred to herein as gray spam. A gray spam predictor is personalized for a given user in vertical learning that uses the user's electronic message behavior and horizontal learning that uses other users' message behavior. The gray spam predictor can be used to predict whether a new message for the user is, or is not, gray spam. A confidence in a prediction may be used in determining the disposition of the message, such as and without limitation placing the message in a spam folder, a gray spam folder and/or requesting input from the user regarding the disposition of the message, for example.

Method and apparatus for predicting unwanted electronic messages for a user

A device, comprising: an array of cells, wherein the cells are arranged in columns and rows; wherein each cell comprises a memristive device; an interfacing circuit that is coupled to each cell of the array of cells; wherein the interfacing circuit is arranged to: receive or generate first variables and second variables; generate memristive device input signals that once provided to memristive devices of the array will cause a change in a state variable of each of the memristive devices of the cells of the array, wherein the change in the state variable of each of the memristive devices of the cells of array reflects a product of one of the first variables and one of the second variables; provide the memristive device input signals to memristive devices of the array; and receive output signals that are a function of at least products of the first variables and the second variables.

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Haifa, Israel

Dotan Di Castro