Seoul National University

Samsung Electronics Co., Ltd.

Sungroh Yoon

Hyeokjun Choe

Seijoon Kim

Seongsik Park

Eunji Kim

Ho Bae

Dahuin Jung

Heeseung Kim

A method of operating a storage device including a neural network processor includes outputting, by a controller device, a trigger signal instructing the neural network processor to perform a neural network operation in response to a command from a host device, requesting, by a neural network processor, target model data about parameters of a target model and instruction data for instructing the neural network operation to a memory device storing the target model data and the instruction data in response to the trigger signal, receiving, by the neural network processor, the target model data and the instruction data from the memory device and outputting, by the neural network processor, inference data based on the target model data and the instruction data.

Storage device and method of operating the same

A computing device trains a position optimization model for determining, from among a plurality of positions, one or more optimal positions on a face based on a training data set including facial skin strain data at the plurality of positions. The computing device trains a speech classification model for classifying a voice from the facial skin strain data based on the training data at the one or more optimal positions determined by the position optimization model among the training data set.

Speech recognition using facial skin strain data

Disclosed is a hidden data removal method based on an autoregressive generative model which is performed by a computer device. The hidden data removal method includes receiving a source image, randomly selecting a target pixel from the source image, and inputting the source image and an identifier of the target pixel to an autoregressive generative model and restoring the target pixel from the source image. The source image is an image in which steganography-based data is hidden, and the autoregressive generative model restores the target pixel on the basis of a pixel value distribution for pixels adjacent to the target pixel in the source image.

Method and apparatus for removing hidden data based on autoregressive generative model

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Seoul, South Korea

Sungroh Yoon