公开(公告)号：US20220066718A1

公开(公告)日：2022-03-03

申请号：US17275011

申请日：2019-09-09

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Hye Ryung KIM ,  Jae JULIEN ,  Yoo Jin CHOI ,  Byeong Ju LEE ,  Jean-Christophe NAOUR

IPC: G06F3/14 ,  G06F3/0484 ,  G06F3/0482

Abstract: The present disclosure provides a server through which a user can increase the usability of a content in a display apparatus by providing the content output from the display apparatus based on upload image data, a display apparatus, and a method of controlling the display apparatus. The display apparatus includes a display; a communication interface configured to communicate with the server; and a controller configured to receive a content generated in the server based on image data uploaded by a user to the server, categories selected by the user, and setting information, and to control the content to be output on the display with the setting defined in the setting information.

公开(公告)号：US20210295173A1

公开(公告)日：2021-09-23

申请号：US17021686

申请日：2020-09-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yoo Jin CHOI ,  Jihwan CHOI ,  Mostafa EL-KHAMY ,  Jungwon LEE

IPC: G06N3/08 ,  G06N3/04

Abstract: A method and system are provided. The method includes receiving, at a generator, a random input, producing, at the generator, a synthetic output of the received random input, receiving, at a teacher network, the synthetic output, receiving, at a student network, the synthetic output, minimizing a maximum of a distance between an output of the teacher network and an output of the student network, and constraining the generator.

公开(公告)号：US20200285894A1

公开(公告)日：2020-09-10

申请号：US16452005

申请日：2019-06-25

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa EL-KHAMY ,  Jungwon LEE ,  Yoo Jin CHOI ,  Haoyu REN

IPC: G06K9/62 ,  G06N3/08 ,  G06K9/46

Abstract: A method and apparatus for providing a rotational invariant neural network is herein disclosed. According to one embodiment, a method includes receiving a first input of an image in a first orientation and training a kernel to be symmetric such that an output corresponding to the first input is the same as an output corresponding to a second input of the image in a second orientation.

公开(公告)号：US20180107925A1

公开(公告)日：2018-04-19

申请号：US15433531

申请日：2017-02-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yoo Jin CHOI ,  Mostafa EL-KHAMY ,  Jungwon LEE

IPC: G06N3/08 ,  G06F17/16

CPC classification number: G06N3/08 ,  G06F17/16 ,  G06N3/063 ,  G06N3/082 ,  G06N3/084

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for performing network parameter quantization in deep neural networks are described. In one aspect, diagonals of a second-order partial derivative matrix (a Hessian matrix) of a loss function of network parameters of a neural network are determined and then used to weight (Hessian-weighting) the network parameters as part of quantizing the network parameters. In another aspect, the neural network is trained using first and second moment estimates of gradients of the network parameters and then the second moment estimates are used to weight the network parameters as part of quantizing the network parameters. In yet another aspect, network parameter quantization is performed by using an entropy-constrained scalar quantization (ECSQ) iterative algorithm. In yet another aspect, network parameter quantization is performed by quantizing the network parameters of all layers of a deep neural network together at once.

公开(公告)号：US20230214713A1

公开(公告)日：2023-07-06

申请号：US17824558

申请日：2022-05-25

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yoo Jin CHOI ,  Mostafa EL-KHAMY ,  Sijia WANG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A server and method thereof are provided for use in a federated network. A method includes receiving local updates from client devices; updating a global model based on the received local updates; quantizing the updated global model; reconstructing feature maps based on the received local updates; refining the quantized, updated global model based on the reconstructed feature maps; and transmitting the refined, quantized, updated global model to the client devices.

公开(公告)号：US20210210505A1

公开(公告)日：2021-07-08

申请号：US16995057

申请日：2020-08-17

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yoo Jin CHOI ,  Jung-Hwan LEE

IPC: H01L27/11582 ,  H01L27/11556 ,  H01L27/11573 ,  H01L27/11526 ,  H01L27/11565 ,  H01L27/11519

Abstract: A nonvolatile memory device and method for fabricating the same are provided. The nonvolatile memory device comprising: a substrate; a mold structure including a first insulating pattern and a plurality of gate electrodes alternately stacked in a first direction on the substrate; and a word line cut region which extends in a second direction different from the first direction and cuts the mold structure, wherein the word line cut region includes a common source line, and the common source line includes a second insulating pattern extending in the second direction, and a conductive pattern extending in the second direction and being in contact with the second insulating pattern and a cross-section in the second direction.

公开(公告)号：US20220067582A1

公开(公告)日：2022-03-03

申请号：US17156126

申请日：2021-01-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yoo Jin CHOI ,  Mostafa El-Khamy ,  Sijia Wang ,  Jungwon Lee

IPC: G06N20/00 ,  G06K9/62

Abstract: Methods and apparatuses are provided for continual few-shot learning. A model for a base task is generated with base classification weights for base classes of the base task. A series of novel tasks is sequentially received. Upon receiving each novel task in the series of novel tasks, the model is updated with novel classification weights for novel classes of the respective novel task. The novel classification weights are generated by a weight generator based on one or more of the base classification weights and, when one or more other novel tasks in the series are previously received, one or more other novel classification weights for novel classes of the one or more other novel tasks. Additionally, for each novel task, a first set of samples of the respective novel task are classified into the novel classes using the updated model.

公开(公告)号：US20200304147A1

公开(公告)日：2020-09-24

申请号：US16576166

申请日：2019-09-19

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yoo Jin CHOI ,  Mostafa EL-KHAMY ,  Jungwon LEE

IPC: H03M7/30 ,  H04N19/13 ,  H04N19/147 ,  H04N19/19 ,  H04L1/00 ,  G06N3/04 ,  G06N3/08

Abstract: A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method includes training a conditional autoencoder using a Lagrange multiplier and training a neural network that includes the conditional autoencoder with mixed quantization bin sizes.

公开(公告)号：US20180107926A1

公开(公告)日：2018-04-19

申请号：US15697035

申请日：2017-09-06

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yoo Jin CHOI ,  Mostafa El-Khamy ,  Jungwon LEE

IPC: G06N3/08 ,  G06F7/58

CPC classification number: G06N3/08 ,  G06F7/582 ,  G06N3/0472 ,  G06N3/063

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for performing network parameter quantization in deep neural networks are described. In one aspect, multi-dimensional vectors representing network parameters are constructed from a trained neural network model. The multi-dimensional vectors are quantized to obtain shared quantized vectors as cluster centers, which are fine-tuned. The fine-tuned and shared quantized vectors/cluster centers are then encoded. Decoding reverses the process.

公开(公告)号：US20230244951A1

公开(公告)日：2023-08-03

申请号：US18099631

申请日：2023-01-20

Applicant: Samsung Electronics Co., Ltd. ,  Duke University

Inventor： Yoo Jin CHOI ,  Mostafa EL-KHAMY ,  Sijia WANG ,  Ricardo Henao GIRALDO ,  Junya CHEN

IPC: G06N3/096 ,  G06N3/042

CPC classification number: G06N3/096 ,  G06N3/042

Abstract: Disclosed is a method and apparatus for dynamic models to identify similar tasks when no task identifier is provided during the training phase in continual learning (CL). The method includes maintaining a memory comprising one or more previously learned tasks, determining, in response to receiving a new task, one of more similarities between at least one previously learned task and the new task, generating, based on the one or more similarities determined and a previously used task-specific encoder corresponding to the at least one previously learned task, a test error value for classifying the new task, and applying the previously used task-specific encoder to the new task based on the generated test error value.