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公开(公告)号：US20240330649A1
公开(公告)日：2024-10-03
申请号：US18610804
申请日：2024-03-20
Inventor： Jeongmin YANG , Hyun Woo KIM , Hwajeon SONG , Byunghyun YOO , Euisok CHUNG , Ran HAN
IPC: G06N3/043
CPC classification number: G06N3/043
Abstract: Provided is an inference method employing a prompt-based meta-learning network and a computer system. The inference method includes selecting a task, generating a prompt key for the selected task using a prompt-embedding network (PEN), calculating similarities between the prompt key for the selected task and prompt keys included in a prompt key pool (PKP), acquiring a prompt value for the selected task using a memory network (MN), and generating an inference result for the selected task using a model-agnostic meta-learning (MAML)-based pre-trained model (MPM).
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2.发明申请公开(公告)号：US20250097008A1
公开(公告)日：2025-03-20
申请号：US18949266
申请日：2024-11-15
Inventor： Kyu Sung LEE , Seong Cheon PARK , Shin Yuk KANG , Hyun Woo KIM , Su Yeon JANG
IPC: H04L9/00 , B29C64/393 , B33Y50/02 , H04L9/08
Abstract: Provided are a three-dimensional (3D) data generation device, an analysis server, and a 3D printing method employing a homomorphic encryption scheme. The 3D data generation device includes a memory, a scan module, and a processor connected to the memory and the scan module. The processor generates 3D data of an object scanned through the scan module and encrypts the 3D data in accordance with a homomorphic encryption scheme.
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3.发明公开公开(公告)号：US20240202454A1
公开(公告)日：2024-06-20
申请号：US18471538
申请日：2023-09-21
Inventor： Euisok CHUNG , Hyun Woo KIM , Hwajeon SONG , Jeongmin YANG , Byunghyun YOO , Ran HAN
IPC: G06F40/30 , G06F40/284
CPC classification number: G06F40/30 , G06F40/284
Abstract: A domain adaptation procedure, such as fine-tuning training, is required to utilize a large-capacity PLM for a specific domain. Attempts in existing research have been made to improve performance of a PLM through domain adaptor technology based on an N-gram in order to reduce errors on the basis of the results of domain text error analysis of the PLM. Proposed is a method of selecting a semantic chunk through a domain semantic chunk graph and PageRank based on the existing domain adaptor research, with an N-gram as the semantic chunk. Proposed is also a method of domain-adapting a large-capacity PLM using semantic chunk dynamic weight masking, which reflects an output value of a PLM rather than simply integrating embedding values of semantic chunks, in a semantic chunk domain adaptor technology.
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公开(公告)号：US20210089933A1
公开(公告)日：2021-03-25
申请号：US16902513
申请日：2020-06-16
Inventor： Hwajeon SONG , Hyun Woo KIM , Euisok CHUNG , Ho Young JUNG , Yunkeun LEE
Abstract: An apparatus for learning procedural knowledge generates procedural knowledge data by connecting unit knowledge that is generated though each episode through interaction with a user, stores the procedural knowledge data generated from each episode in a short-term memory, estimates data to be long-term memorized from the procedural knowledge data stored in the short-term memory, converts the estimated data into long-term memory data, and stores the long-term memory data in a long-term memory.
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公开(公告)号：US20190325025A1
公开(公告)日：2019-10-24
申请号：US16217804
申请日：2018-12-12
Inventor： Ho Young JUNG , Hyun Woo KIM , Hwa Jeon SONG , Eui Sok CHUNG , Jeon Gue PARK
Abstract: Provided are a neural network memory computing system and method. The neural network memory computing system includes a first processor configured to learn a sense-making process on the basis of sense-making multimodal training data stored in a database, receive multiple modalities, and output a sense-making result on the basis of results of the learning, and a second processor configured to generate a sense-making training set for the first processor to increase knowledge for learning a sense-making process and provide the generated sense-making training set to the first processor.
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Patent Agency Ranking
公开(公告)号：US20180247642A1
公开(公告)日：2018-08-30
申请号：US15697923
申请日：2017-09-07
Inventor： Hyun Woo KIM , Ho Young JUNG , Jeon Gue PARK , Yun Keun LEE
CPC classification number: G10L15/16 , G06N3/08 , G06N3/084 , G10L15/02 , G10L15/04 , G10L21/04 , G10L25/84 , G10L2015/025 , G10L2015/027
Abstract: The present invention relates to a method and apparatus for improving spontaneous speech recognition performance. The present invention is directed to providing a method and apparatus for improving spontaneous speech recognition performance by extracting a phase feature as well as a magnitude feature of a voice signal transformed to the frequency domain, detecting a syllabic nucleus on the basis of a deep neural network using a multi-frame output, determining a speaking rate by dividing the number of syllabic nuclei by a voice section interval detected by a voice detector, calculating a length variation or an overlap factor according to the speaking rate, and performing cepstrum length normalization or time scale modification with a voice length appropriate for an acoustic model.
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公开(公告)号：US20240160423A1
公开(公告)日：2024-05-16
申请号：US18505799
申请日：2023-11-09
Inventor： Seong Cheon PARK , Hyun Woo KIM , Su Yeon JANG
IPC: G06F8/54
CPC classification number: G06F8/54
Abstract: Disclosed herein is a program conversion apparatus, which converts a first program to which homomorphic encryption is not applied into a second program to which the homomorphic encryption is applied, including a memory, which stores a first library for configuring the first program and a second library for configuring the second program, and a processor configured to convert the first program into the second program through conversion between operations provided by the first and second libraries.
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公开(公告)号：US20210398004A1
公开(公告)日：2021-12-23
申请号：US17353136
申请日：2021-06-21
Inventor： Hyun Woo KIM , Gyeong Moon PARK , Jeon Gue PARK , Hwa Jeon SONG , Byung Hyun YOO , Eui Sok CHUNG , Ran HAN
Abstract: Provided are a method and apparatus for online Bayesian few-shot learning. The present invention provides a method and apparatus for online Bayesian few-shot learning in which multi-domain-based online learning and few-shot learning are integrated when domains of tasks having data are sequentially given.
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公开(公告)号：US20190318228A1
公开(公告)日：2019-10-17
申请号：US16260637
申请日：2019-01-29
Inventor： Hyun Woo KIM , Ho Young JUNG , Jeon Gue PARK , Yun Keun LEE
Abstract: Provided are an apparatus and method for a statistical memory network. The apparatus includes a stochastic memory, an uncertainty estimator configured to estimate uncertainty information of external input signals from the input signals and provide the uncertainty information of the input signals, a writing controller configured to generate parameters for writing in the stochastic memory using the external input signals and the uncertainty information and generate additional statistics by converting statistics of the external input signals, a writing probability calculator configured to calculate a probability of a writing position of the stochastic memory using the parameters for writing, and a statistic updater configured to update stochastic values composed of an average and a variance of signals in the stochastic memory using the probability of a writing position, the parameters for writing, and the additional statistics.
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公开(公告)号：US20230186154A1
公开(公告)日：2023-06-15
申请号：US17893628
申请日：2022-08-23
Inventor： Byunghyun YOO , Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Jeongmin YANG , Sungwon YI , Euisok CHUNG , Ran HAN
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: An exploration method used by an exploration apparatus in multi-agent reinforcement learning to collect training samples during the training process is provided. The exploration method includes calculating the influence of a selected action of each agent on the actions of other agents in a current state, calculating a linear sum of the value of a utility function representing the action value of each agent and the influence on the actions of the other agent calculated for the selected action of each agent, and obtaining a sample to be used for training an action policy of each agent by probabilistically selecting the action in which the linear sum is the maximum, and the random action.
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