Samsung Electronics Co., Ltd.

New York University

Jaedeok Kim

Chiyoun Park

Hyunjoo Jung

Inkwon Choi

Youngchul Sohn

Jungwook Kim

Juyong Song

Changhyun Lee

Yoonjung Choi

Jaewook Shin

Junho Rim

Aeran Lim

Kyunghyun Cho

Sean Welleck

Jongryul Lee

Yuanzhe Pang

Ilia Kulikov

An electronic apparatus includes at least one memory configured to store at least one instruction and a first neural network model; a communicator comprising communication circuitry; and at least one processor configured to execute the at least one instruction to: receive, from an external electronic device, information on a second neural network model stored in the external electronic device through the communicator; compare the first neural network model with the second neural network model based on the information on the second neural network model; and control the communicator to transmit, to the external electronic device, information on a weight between nodes of the first neural network model based on comparison between the second neural network model and the first neural network model.

Electronic apparatus for applying personalized artificial intelligence model to another model

A method of controlling an electronic apparatus for generating an artificial intelligence model is disclosed. The method includes based on a user command for using a plurality of artificial intelligence models being input, generating the plurality of artificial intelligence models, and inputting input data to the generated artificial intelligence model to obtain output data with respect to the input data, wherein the generating includes, based on a reference value for obtaining a layer parameter with respect to layers at corresponding locations among a plurality of layers included in each of the plurality of artificial intelligence model, generating layers at corresponding locations among layers included in each of the plurality of artificial intelligence models.

Electronic apparatus and method of controlling thereof

An electronic device and a control method therefor are provided. The electronic device may comprise: a memory for storing at least one instruction; and a processor connected to the memory so as to control the electronic device, wherein the processor: by executing the at least one instruction, appends a second layer including a learnable function to a first layer in an artificial neural network including a plurality of layers; updates a parameter value included in the second layer by learning of the artificial neural network; acquires a function value by inputting the updated parameter value to the learnable function; and eliminates at least one channel among a plurality of channels included in the first layer on the basis of the acquired function value so as to achieve update to a third layer.

Electronic device and control method therefor

An electronic apparatus is provided. The apparatus includes a communicator, a memory configured to store a neural network model, an activation function generating module that generates activation function information used in the neural network model, first activation function information, second activation function information, and third activation function information generated based on the first activation function information and the second activation function information, and a processor configured to receive fourth activation function information from an external device through the communicator, based on type information on the fourth activation function information corresponding to the second activation function information, input the first activation function information and the fourth activation function information to the activation function generating module to generate fifth activation function information, and control the neural network model to change the activation function information used in the neural network model from the third activation function information to the fifth activation function information.

Disclosed is an electronic apparatus. The electronic apparatus may include a memory configured to store one or more training data generation models and an artificial intelligence model, and a processor configured to generate personal training data that reflects a characteristic of a user using the one or more training data generation models, train the artificial intelligence model using the personal learning data as training data, and store the trained artificial intelligence model in the memory.

Method and apparatus for artificial intelligence model personalization

An electronic device for reconstructing an artificial intelligence model, and a control method thereof are provided. The control method includes inputting at least one input data to a first artificial intelligence (AI) model, to acquire at least one output data, acquiring first usage information, based on the acquired at least one output data, acquiring first reconstruction information for reconstructing the first AI model, based on the acquired first usage information, and reconstructing the first AI model, based on the acquired first reconstruction information, to acquire a second AI model.

Electronic device for reconstructing an artificial intelligence model and a control method thereof

Provided is an apparatus configured to determine a common neural network based on a comparison between a first neural network included in a first application program and a second neural network included in a second application program, utilize the common neural network when the first application program or the second application program is executed.

Apparatus and method for managing application program

A method of obtaining, by an electronic device, a sentence corresponding to context information, including obtaining first output information including at least one word output by decoding the context information based on at least one data; based on detecting that a first token is not included in the first output information, determining whether a number of words included in the first output information is greater than or equal to a reference value; based on a result of the determining, replacing the at least one data with other data; and obtaining the sentence corresponding to the context information based on at least one output information obtained by decoding the context information based on the other data.

Electronic device for obtaining sentence corresponding to context information and operating method thereof

An electronic apparatus for compressing a neural network model may acquire training data pairs based on an original, trained neural network model and train a compressed neural network model compressed from the original, trained neural network model using the acquired training data pairs.

Neural network model apparatus and compressing method of neural network model

The electronic device includes a camera, a non-volatile memory storing at least one instruction and a plurality of object recognition models, a volatile memory, and a processor, connected to the non-volatile memory, the volatile memory, and the camera, and configured to control the electronic device. The processor, by executing the at least one instruction, is configured to, based on an operation mode of the electronic device, load, to the volatile memory, a hierarchical object recognition model having a hierarchical structure corresponding to the operation mode, the hierarchical object recognition model including objection recognition models among the plurality of object recognition models, obtain information on an object by inputting an object image obtained through the camera to the hierarchical object recognition model, and determine an operation of the electronic device based on the information on the object.

Electronic device and method for controlling the electronic device

A method for controlling an electronic apparatus includes storing a plurality of artificial intelligence models in a first memory, based on receiving a control signal for loading a first artificial intelligence model among the plurality of stored artificial intelligence models into a second memory, identifying an available memory size of the second memory, and based on a size of the first artificial intelligence model being larger than the available memory size of the second memory, obtaining a first compression artificial intelligence model by compressing the first artificial intelligence model based om the available memory size of the second memory, and loading the first compression artificial intelligence model into the second memory.

Growing community of inventors

Suwon-si, South Korea

Jaedeok Kim