Nokia Technologies Oy

Nokia Solutions and Networks Oy

Nanyang Technological University

Continental Automotive Technologies Gmbh

Continental Autonomous Mobility Germany Gmbh

Csaba Nemes

Regina Deak-Meszlenyi

Sinno Jialin Pan

Sándor Jordán

David Ivan

Laszlo Kecskes

Istvan Ketyko

Gabor Oravecz

Shen Ren

A method for determining an encoder architecture of a convolutional neural network configured to process image processing tasks. For each image processing task), characteristic scale distribution is calculated based on training data. Encoder architecture candidates are generated, each including a shared encoder layer providing computational operations for image processing tasks and branches which span over encoder layers providing at least partly different computational operations for the image processing tasks. Each branch is associated with a certain image processing task. Receptive encoder layer field sizes and assessment measures are calculated, each assessment measure referring to a combination of a certain encoder architecture and a certain image processing task, and including information regarding matching quality of characteristic scale distribution associated with the assessment measure to the receptive field sizes of the encoder layers. The assessment measures are compared and a comparison result established. An encoder architecture is selected based on the comparison result.

Method for determining the encoder architecture of a neural network

A computer-implemented method includes receiving data generated using at least one sensor of a vehicle; and simultaneously performing multiple different prediction tasks on the data using a multi-task neural network, wherein the multi-task neural network comprises at least one shared parameter inference matrix comprising parameters shared between the multiple different prediction tasks, and the at least one shared parameter inference matrix was over-parameterized during training into at least one shared parameter matrix and multiple task-specific parameter matrices, each of the multiple task-specific parameter matrices being associated with a different one of the multiple different tasks.

Systems and methods for deep multi-task learning for embedded machine vision applications

A method in a telecommunications network, the network including at least one service area, the method comprising: at the network, distributing a mobile edge computing server within a corresponding one of the service areas; at the mobile edge computing server, distributing at least one access point within the corresponding service area; and at the mobile edge computing server, determining a mobile edge computing area within the corresponding service area; wherein at least one of the access points is located within a corresponding one of the mobile edge computing areas.

Method, apparatus and system for mobile edge computing

Methods and apparatus, including computer program products, are provided for watermarking neural networks. In some embodiments, there may be provided a method. The method may include determining, for a neural network, an activation layer output by a hidden layer of the neural network. The method may include selecting a watermarking process. The method may include applying the selected watermarking process to the activation layer output to generate a key. The method may include storing, for the neural network to enable detection of copying of the neural network, the selected watermarking process and the key. Related systems, methods, and articles of manufacture are also described.

Neural network watermarking

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Dunakeszi, Hungary

Csaba Nemes