Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Wi, Hyowon"'
Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-range information. Prior works h
Externí odkaz:
http://arxiv.org/abs/2406.03671
Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stoc
Externí odkaz:
http://arxiv.org/abs/2405.00287
Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by using self-supervised signals from raw data. Integration of CL with graph convolutional network (GCN)-based
Externí odkaz:
http://arxiv.org/abs/2312.16563
Time series imputation is one of the most fundamental tasks for time series. Real-world time series datasets are frequently incomplete (or irregular with missing observations), in which case imputation is strongly required. Many different time series
Externí odkaz:
http://arxiv.org/abs/2312.16581
Sequential recommendation (SR) models based on Transformers have achieved remarkable successes. The self-attention mechanism of Transformers for computer vision and natural language processing suffers from the oversmoothing problem, i.e., hidden repr
Externí odkaz:
http://arxiv.org/abs/2312.10325
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encod
Externí odkaz:
http://arxiv.org/abs/2312.07753
Autor:
Choi, Jeongwhan, Wi, Hyowon, Kim, Jayoung, Shin, Yehjin, Lee, Kookjin, Trask, Nathaniel, Park, Noseong
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc. However, one of the challenges with deep Transforme
Externí odkaz:
http://arxiv.org/abs/2312.04234
Autor:
Lim, Seonkyu, Park, Jaehyeon, Kim, Seojin, Wi, Hyowon, Lim, Haksoo, Jeon, Jinsung, Choi, Jeongwhan, Park, Noseong
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performanc
Externí odkaz:
http://arxiv.org/abs/2311.04522
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