Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Hong, Seoyoung"'
Collaborative filtering (CF) methods for recommendation systems have been extensively researched, ranging from matrix factorization and autoencoder-based to graph filtering-based methods. Recently, lightweight methods that require almost no training
Externí odkaz:
http://arxiv.org/abs/2405.04746
Graph neural networks (GNNs) are one of the most popular research topics for deep learning. GNN methods typically have been designed on top of the graph signal processing theory. In particular, diffusion equations have been widely used for designing
Externí odkaz:
http://arxiv.org/abs/2211.14208
Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods. Being inspired by recent successe
Externí odkaz:
http://arxiv.org/abs/2211.09324
Autor:
Hong, Seoyoung, Jo, Minju, Kook, Seungji, Jung, Jaeeun, Wi, Hyowon, Park, Noseong, Cho, Sung-Bae
Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filterin
Externí odkaz:
http://arxiv.org/abs/2211.04266
Many U.S. metropolitan cities are notorious for their severe shortage of parking spots. To this end, we present a proactive prediction-driven optimization framework to dynamically adjust parking prices. We use state-of-the-art deep learning technolog
Externí odkaz:
http://arxiv.org/abs/2208.14231
Neural networks inspired by differential equations have proliferated for the past several years. Neural ordinary differential equations (NODEs) and neural controlled differential equations (NCDEs) are two representative examples of them. In theory, N
Externí odkaz:
http://arxiv.org/abs/2109.01876
Autor:
Li, Duanshun, Liu, Jing, Jeon, Jinsung, Hong, Seoyoung, Le, Thai, Lee, Dongwon, Park, Noseong
We present a prediction-driven optimization framework to maximize the market influence in the US domestic air passenger transportation market by adjusting flight frequencies. At the lower level, our neural networks consider a wide variety of features
Externí odkaz:
http://arxiv.org/abs/2105.15012
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