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29
resultados
Última actualización
30/09/2025 [07:19:00]
Resultados 25 a 29 de 29
Resumen de: WO2025181729A1
A method implemented on a computer system, the method comprising the following steps: receiving one or more parameters of a bone in which an implant is to be placed, generating an input to an artificial neural network (ANN)- based optimization framework based at least in part on the one or more parameters, and receiving, based at least in part on the input, an output of the ANN-based optimization framework indicating a relative density and a topology for a triply periodic minimal surface (TPMS) architecture for the implant.
Resumen de: EP4610887A1
The present disclosure provides a method and system for training a graph neural network and a method of identifying an abnormal account. The method of training a graph neural network includes: obtaining initial graph structure data corresponding to the terminal device, the initial graph structure data respectively obtained by the plurality of distributed training terminals being derived from the same sample graph structure data; and performing the following graph structure data processing stage and graph neural network training stage cyclically, until a target neural network satisfying a training requirement is obtained: determining a processing opportunity for currently performing a graph structure data processing stage based on historical execution data of historically performing a graph structure data processing stage and a graph neural network training stage; performing, based on the processing opportunity, graph structure data processing on the initial graph structure data in the graph structure data processing stage, to generate target graph structure data; the graph structure data processing comprising data sampling processing and feature extraction processing; and training, based on the target graph structure data, the target neural network in the graph neural network training stage.
Resumen de: EP4610881A1
A method (600) and system (100) of compressing a first deep learning (DL) model is disclosed. A processor receives a verified DL model. The verified DL model is converted into a standard DL model based on a framework corresponding to a plurality of provisional compression types. A compression strategy is selected from a plurality of compression strategies using a neural network (NN) based on determining a compression feature vector based on a knowledge graph. A concatenated vector is determined based on a model feature vector, a dataset feature vector and compression feature vector. The NN is trained based on the concatenated vector. A bias of the NN is trained based on a model score corresponding to the standard NN. A compression embedding is determined corresponding to the selected compression strategy.
Nº publicación: EP4610890A1 03/09/2025
Solicitante:
SAMSUNG ELECTRONICS CO LTD [KR]
Samsung Electronics Co., Ltd
Resumen de: EP4610890A1
Provided are a method and device for controlling inference task execution through split inference of an artificial neural network. The method includes determining one policy from among a plurality of task execution policies based on at least one of requirements of the inference task and a correction index, wherein the correction index indicates a failure rate of each task execution policy, determining, based on the policy, one or more devices to execute split inference of the artificial neural network, updating the correction index corresponding to the policy based on a result of whether the split inference executed by the one or more devices has failed, and updating the policy by using execution records of the split inference obtained from the one or more devices, wherein the execution records include information on a cause of failure of the split inference. Also, the method of controlling execution of an inference task through split inference of an artificial neural network of the electronic device may be performed using an artificial intelligence model.
BOPI
Sede Electrónica
