6 citations · 10 across the 10 of their papers we have counts for
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To Predict or Not To Predict? Proportionally Masked Autoencoders for Tabular Data Imputation
Jungkyu Kim, Kibok Lee, Taeyoung Park
Masked autoencoders (MAEs) have recently demonstrated effectiveness in tabular data imputation. However, due to the inherent heterogeneity of tabular data, the uniform random maski…
Sequential Order-Robust Mamba for Time Series Forecasting
Seunghan Lee, Juri Hong, Kibok Lee +1
Mamba has recently emerged as a promising alternative to Transformers, offering near-linear complexity in processing sequential data. However, while channels in time series (TS) da…
ANT: Adaptive Noise Schedule for Time Series Diffusion Models
Seunghan Lee, Kibok Lee, Taeyoung Park
Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various t…
Dataset-Driven Channel Masks in Transformers for Multivariate Time Series
Seunghan Lee, Taeyoung Park, Kibok Lee
Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale TS datasets. However, previous…