Výsledky vyhledávání - "Park Jonghun"

Akademický článek

Sustainability and Industry 4.0 in the packaging and printing industry: a diagnostic survey in Poland

Autor: Gladysz Bartlomiej, Krystosiak Krzysztof, Buczacki Aleksander, Quadrini Walter, Ejsmont Krzysztof, Kluczek Aldona, Park Jonghun, Fumagalli Luca

Publikováno v: Engineering Management in Production and Services, Vol 16, Iss 2, Pp 51-67 (2024)

Industry 4.0 (I4.0) became an important paradigm to bridge the gap between technologies and humans. The paper aims to diagnose sustainability performance and I4.0 maturity in Poland’s printing and packaging sector and identify research areas where

Externí odkaz: https://doaj.org/article/4ace055e6ec0417f9ac21d83ff37f8f1

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e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce

Autor: Shin, Wonyoung, Park, Jonghun, Woo, Taekang, Cho, Yongwoo, Oh, Kwangjin, Song, Hwanjun

Understanding vision and language representations of product content is vital for search and recommendation applications in e-commerce. As a backbone for online shopping platforms and inspired by the recent success in representation learning research

Externí odkaz: http://arxiv.org/abs/2207.00208

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Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation

Autor: Park, Heewoong, Cho, Sukhyun, Park, Jonghun

Inferring the probability distribution of sentences or word sequences is a key process in natural language processing. While word-level language models (LMs) have been widely adopted for computing the joint probabilities of word sequences, they have

Externí odkaz: http://arxiv.org/abs/2103.07875

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Image-to-Image Retrieval by Learning Similarity between Scene Graphs

Autor: Yoon, Sangwoong, Kang, Woo Young, Jeon, Sungwook, Lee, SeongEun, Han, Changjin, Park, Jonghun, Kim, Eun-Sol

As a scene graph compactly summarizes the high-level content of an image in a structured and symbolic manner, the similarity between scene graphs of two images reflects the relevance of their contents. Based on this idea, we propose a novel approach

Externí odkaz: http://arxiv.org/abs/2012.14700

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Quantum-classical reinforcement learning for decoding noisy classical parity information

Autor: Park, Daniel K., Park, Jonghun, Rhee, June-Koo Kevin

Publikováno v: Quantum Machine Intelligence 2, 1-11 (2020)

Learning a hidden parity function from noisy data, known as learning parity with noise (LPN), is an example of intelligent behavior that aims to generalize a concept based on noisy examples. The solution to LPN immediately leads to decoding a random

Externí odkaz: http://arxiv.org/abs/1910.00781

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A Bi-directional Transformer for Musical Chord Recognition

Autor: Park, Jonggwon, Choi, Kyoyun, Jeon, Sungwook, Kim, Dokyun, Park, Jonghun

Chord recognition is an important task since chords are highly abstract and descriptive features of music. For effective chord recognition, it is essential to utilize relevant context in audio sequence. While various machine learning models such as c

Externí odkaz: http://arxiv.org/abs/1907.02698

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