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pro vyhledávání: '"Shin, Yehjin"'
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:
Kim, Jayoung, Lee, Chaejeong, Shin, Yehjin, Park, Sewon, Kim, Minjung, Park, Noseong, Cho, Jihoon
Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big
Externí odkaz:
http://arxiv.org/abs/2206.08555