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1.发明公开公开(公告)号：US20230281477A1
公开(公告)日：2023-09-07
申请号：US18103962
申请日：2023-01-31
Inventor： Chun-Hee LEE , Dong-oh KANG , Hwa Jeon SONG
Abstract: A learning method for improving performance of a knowledge graph embedding model is provided. The method includes: performing learning of a first knowledge graph embedding model based on input knowledge data; extracting all embedding vectors from the learned first knowledge graph embedding model, and extracting prior knowledge based on the extracted embedding vectors; and performing learning of a second knowledge graph embedding model through at least one of initialization of the embedding vectors and transform of the input knowledge data based on the extracted prior knowledge.
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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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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：US20170206894A1
公开(公告)日：2017-07-20
申请号：US15187581
申请日：2016-06-20
Inventor： Byung Ok KANG , Jeon Gue PARK , Hwa Jeon SONG , Yun Keun LEE , Eui Sok CHUNG
CPC classification number: G10L15/16 , G10L15/063 , G10L15/07 , G10L2015/022 , G10L2015/0636
Abstract: A speech recognition apparatus based on a deep-neural-network (DNN) sound model includes a memory and a processor. As the processor executes a program stored in the memory, the processor generates sound-model state sets corresponding to a plurality of pieces of set training speech data included in multi-set training speech data, generates a multi-set state cluster from the sound-model state sets, and sets the multi-set training speech data as an input node and the multi-set state cluster as output nodes so as to learn a DNN structured parameter.
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5.发明公开公开(公告)号：US20240160859A1
公开(公告)日：2024-05-16
申请号：US18507953
申请日：2023-11-13
Inventor： Eui Sok CHUNG , Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Jeong Min YANG , Byung Hyun YOO , Ran HAN
IPC: G06F40/40
CPC classification number: G06F40/40
Abstract: The present invention relates to a multi-modality system for recommending multiple items using an interaction and a method of operating the same. The multi-modality system includes an interaction data preprocessing module that preprocesses an interaction data set and converts the preprocessed interaction data set into interaction training data; an item data preprocessing module that preprocesses item information data and converts the preprocessed item information data into item training data; and a learning module that includes a neural network model that is trained using the interaction training data and the item training data and outputs a result including a set of recommended items using a conversation context with a user as input.
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Patent Agency Ranking
公开(公告)号：US20210374545A1
公开(公告)日：2021-12-02
申请号：US17332464
申请日：2021-05-27
Inventor： Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Yoo Rhee OH , Byung Hyun YOO , Eui Sok CHUNG , Ran HAN
Abstract: A knowledge increasing method includes calculating uncertainty of knowledge obtained from a neural network using an explicit memory, determining the insufficiency of the knowledge on the basis of the calculated uncertainty, obtaining additional data (learning data) for increasing insufficient knowledge, and training the neural network by using the additional data to autonomously increase knowledge.
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公开(公告)号：US20210089904A1
公开(公告)日：2021-03-25
申请号：US17024062
申请日：2020-09-17
Inventor： Eui Sok CHUNG , Hyun Woo KIM , Hwa Jeon SONG , Yoo Rhee OH , Byung Hyun YOO , Ran HAN
Abstract: The present invention provides a new learning method where regularization of a conventional model is reinforced by using an adversarial learning method. Also, a conventional method has a problem of word embedding having only a single meaning, but the present invention solves a problem of the related art by applying a self-attention model.
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公开(公告)号：US20170200458A1
公开(公告)日：2017-07-13
申请号：US15186286
申请日：2016-06-17
Inventor： Jeom Ja KANG , Hwa Jeon SONG , Jeon Gue PARK , Hoon CHUNG
IPC: G10L25/87 , G10L15/02 , G10L15/18 , G10L15/197
CPC classification number: G10L25/87 , G10L15/02 , G10L15/1815 , G10L15/197 , G10L15/22 , G10L25/48
Abstract: An apparatus and method for verifying an utterance based on multi-event detection information in a natural language speech recognition system. The apparatus includes a noise processor configured to process noise of an input speech signal, a feature extractor configured to extract features of speech data obtained through the noise processing, an event detector configured to detect events of the plurality of speech features occurring in the speech data using the noise-processed data and data of the extracted features, a decoder configured to perform speech recognition using a plurality of preset speech recognition models for the extracted feature data, and an utterance verifier configured to calculate confidence measurement values in units of words and sentences using information on the plurality of events detected by the event detector and a preset utterance verification model and perform utterance verification according to the calculated confidence measurement values.
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公开(公告)号：US20140221043A1
公开(公告)日：2014-08-07
申请号：US14018068
申请日：2013-09-04
Inventor： Hwa Jeon SONG , Ho Young Jung , Yun Keun Lee
CPC classification number: H04M1/72519 , G10L15/25 , H04M2250/52 , H04M2250/74
Abstract: Provided is a mobile communication terminal including: a camera module which captures an image of a set area; a microphone module which, when a sound including a voice of a user is input, extracts a sound level corresponding to the sound and a sound generating position; and a control module which estimates a position of a lip of the user from the image, extracts a voice level from the sound level corresponding to the position of the lip of the user and a voice generating position from the sound generating position, and recognizes the voice of the user based on at least one of the voice level and the voice generating position.
Abstract translation: 提供了一种移动通信终端，包括：相机模块，其捕获设置区域的图像; 麦克风模块，当输入包括用户的声音的声音时，提取与声音和声音产生位置相对应的声级; 以及控制模块，其从图像估计用户的嘴唇的位置，从与声音产生位置的用户的嘴唇的位置和语音产生位置相对应的声级提取语音电平，并且识别出 基于语音电平和语音产生位置中的至少一个的用户的语音。
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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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